{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 糖尿病遗传风险预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-1-2abba71d7e4a>:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['血糖'] = 1\n",
      "<ipython-input-1-2abba71d7e4a>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['血糖'] = 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='血糖', ylabel='年龄'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 读取数据\n",
    "train = pd.read_csv('../data/train.csv', encoding='gbk')\n",
    "train = train.drop(['id'], axis=1)\n",
    "train['性别'] = train['性别'].map({'男':1, '女':0})\n",
    "\n",
    "# 血糖正常值是指人空腹的时候血糖值在3.9～6.1毫摩尔/升\n",
    "data1=train[train['血糖'] > 6.1]\n",
    "data1['血糖'] = 1\n",
    "data2=train[train['血糖'] < 3.9]\n",
    "data2['血糖'] = 0\n",
    "df = pd.concat([data1, data2])\n",
    "\n",
    "# 绘制柱状图和箱线图分析性别与血糖、年龄与血糖的关系\n",
    "fig=plt.figure(figsize=(14, 14))  # 设置画布大小\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "fig.add_subplot(1, 2, 1)\n",
    "sns.countplot(x='血糖', data=df, hue='性别', palette='rainbow')\n",
    "fig.add_subplot(1, 2, 2)\n",
    "sns.boxplot(x='血糖', y='年龄', data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "*r-谷氨酰基转换酶     0.140024\n",
       "*丙氨酸氨基转换酶      0.124791\n",
       "*天门冬氨酸氨基转换酶    0.091724\n",
       "*总蛋白           0.047471\n",
       "*球蛋白           0.053508\n",
       "*碱性磷酸酶         0.151119\n",
       "中性粒细胞%         0.047878\n",
       "乙肝e抗体         -0.022560\n",
       "乙肝e抗原          0.000895\n",
       "乙肝核心抗体         0.009570\n",
       "乙肝表面抗体        -0.052420\n",
       "乙肝表面抗原         0.023518\n",
       "低密度脂蛋白胆固醇      0.167802\n",
       "单核细胞%          0.001301\n",
       "嗜碱细胞%          0.023881\n",
       "嗜酸细胞%          0.005460\n",
       "尿素             0.154036\n",
       "尿酸             0.027109\n",
       "年龄             0.250452\n",
       "性别             0.141627\n",
       "总胆固醇           0.163226\n",
       "淋巴细胞%         -0.053933\n",
       "甘油三酯           0.249543\n",
       "白球比例          -0.020111\n",
       "白细胞计数          0.093266\n",
       "白蛋白            0.000252\n",
       "红细胞体积分布宽度     -0.070134\n",
       "红细胞压积          0.126097\n",
       "红细胞平均体积       -0.009815\n",
       "红细胞平均血红蛋白浓度    0.144198\n",
       "红细胞平均血红蛋白量     0.062811\n",
       "红细胞计数          0.129056\n",
       "肌酐             0.105233\n",
       "血小板体积分布宽度      0.036732\n",
       "血小板平均体积        0.026312\n",
       "血小板比积         -0.066777\n",
       "血小板计数         -0.076223\n",
       "血红蛋白           0.152096\n",
       "高密度脂蛋白胆固醇     -0.084771\n",
       "Name: 血糖, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations = train.corr()['血糖'].drop('血糖')\n",
    "correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 读取数据\n",
    "train = pd.read_csv('../data/train.csv', encoding='gbk')\n",
    "test = pd.read_csv('../data/test.csv', encoding='gbk')\n",
    "def make_feat(train, test):\n",
    "    train_id = train.id.values.copy()\n",
    "    test_id = test.id.values.copy()\n",
    "    data = pd.concat([train, test])  # 对数据进行合并与重塑\n",
    "    data['性别'] = data['性别'].map({'男':1, '女':0})\n",
    "    data=data[data['年龄']>10]\n",
    "    train_feat = data[data.id.isin(train_id)]\n",
    "    test_feat = data[data.id.isin(test_id)]\n",
    "    # 对数据缺失值进行处理\n",
    "    train_feat = train_feat.drop(['id', '体检日期', '乙肝表面抗原', '乙肝表面抗体',\n",
    "                                  '乙肝e抗原', '乙肝e抗体', '乙肝核心抗体'], axis=1)\n",
    "    test_feat = test_feat.drop(['id','体检日期', '乙肝表面抗原', '乙肝表面抗体',\n",
    "                                '乙肝e抗原','乙肝e抗体','乙肝核心抗体'], axis=1)\n",
    "    # 对缺少一部分的数据进行中位数填充\n",
    "    train_feat.fillna(train_feat.median(axis=0), inplace=True)\n",
    "    test_feat.fillna(test_feat.median(axis=0), inplace=True)\n",
    "    return train_feat, test_feat\n",
    "train_feat, test_feat = make_feat(train, test)\n",
    "train_feat.to_csv('../tmp/train_feat.csv', index=False, encoding='gbk')\n",
    "test_feat.to_csv('../tmp/test_feat.csv', index=False, encoding='gbk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>*r-谷氨酰基转换酶</th>\n",
       "      <th>*丙氨酸氨基转换酶</th>\n",
       "      <th>*天门冬氨酸氨基转换酶</th>\n",
       "      <th>*总蛋白</th>\n",
       "      <th>*球蛋白</th>\n",
       "      <th>*碱性磷酸酶</th>\n",
       "      <th>中性粒细胞%</th>\n",
       "      <th>低密度脂蛋白胆固醇</th>\n",
       "      <th>单核细胞%</th>\n",
       "      <th>嗜碱细胞%</th>\n",
       "      <th>...</th>\n",
       "      <th>红细胞平均血红蛋白量</th>\n",
       "      <th>红细胞计数</th>\n",
       "      <th>肌酐</th>\n",
       "      <th>血小板体积分布宽度</th>\n",
       "      <th>血小板平均体积</th>\n",
       "      <th>血小板比积</th>\n",
       "      <th>血小板计数</th>\n",
       "      <th>血糖</th>\n",
       "      <th>血红蛋白</th>\n",
       "      <th>高密度脂蛋白胆固醇</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.23</td>\n",
       "      <td>23.10</td>\n",
       "      <td>24.96</td>\n",
       "      <td>76.88</td>\n",
       "      <td>27.28</td>\n",
       "      <td>99.59</td>\n",
       "      <td>54.1</td>\n",
       "      <td>2.65</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>31.9</td>\n",
       "      <td>5.21</td>\n",
       "      <td>77.25</td>\n",
       "      <td>17.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.164</td>\n",
       "      <td>166.0</td>\n",
       "      <td>6.06</td>\n",
       "      <td>166.1</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.00</td>\n",
       "      <td>36.25</td>\n",
       "      <td>24.57</td>\n",
       "      <td>79.43</td>\n",
       "      <td>31.67</td>\n",
       "      <td>67.21</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>29.9</td>\n",
       "      <td>5.21</td>\n",
       "      <td>87.12</td>\n",
       "      <td>10.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.260</td>\n",
       "      <td>277.0</td>\n",
       "      <td>5.39</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.17</td>\n",
       "      <td>15.23</td>\n",
       "      <td>20.82</td>\n",
       "      <td>86.23</td>\n",
       "      <td>38.23</td>\n",
       "      <td>63.69</td>\n",
       "      <td>48.1</td>\n",
       "      <td>2.01</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>31.3</td>\n",
       "      <td>4.76</td>\n",
       "      <td>78.19</td>\n",
       "      <td>16.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.199</td>\n",
       "      <td>241.0</td>\n",
       "      <td>5.59</td>\n",
       "      <td>148.8</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.22</td>\n",
       "      <td>10.59</td>\n",
       "      <td>14.99</td>\n",
       "      <td>70.98</td>\n",
       "      <td>26.96</td>\n",
       "      <td>74.08</td>\n",
       "      <td>41.7</td>\n",
       "      <td>5.04</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>31.9</td>\n",
       "      <td>4.29</td>\n",
       "      <td>61.46</td>\n",
       "      <td>10.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.260</td>\n",
       "      <td>252.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>137.0</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.72</td>\n",
       "      <td>14.78</td>\n",
       "      <td>20.07</td>\n",
       "      <td>78.05</td>\n",
       "      <td>36.22</td>\n",
       "      <td>75.79</td>\n",
       "      <td>56.6</td>\n",
       "      <td>3.65</td>\n",
       "      <td>9.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>20.6</td>\n",
       "      <td>5.15</td>\n",
       "      <td>77.19</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.350</td>\n",
       "      <td>316.0</td>\n",
       "      <td>5.42</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>26.08</td>\n",
       "      <td>21.45</td>\n",
       "      <td>23.90</td>\n",
       "      <td>76.72</td>\n",
       "      <td>30.86</td>\n",
       "      <td>84.82</td>\n",
       "      <td>52.5</td>\n",
       "      <td>3.33</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>28.9</td>\n",
       "      <td>5.47</td>\n",
       "      <td>95.72</td>\n",
       "      <td>13.2</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.220</td>\n",
       "      <td>208.0</td>\n",
       "      <td>5.31</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>37.05</td>\n",
       "      <td>12.46</td>\n",
       "      <td>19.33</td>\n",
       "      <td>76.14</td>\n",
       "      <td>33.99</td>\n",
       "      <td>146.11</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.51</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>31.7</td>\n",
       "      <td>4.76</td>\n",
       "      <td>63.07</td>\n",
       "      <td>11.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.290</td>\n",
       "      <td>276.0</td>\n",
       "      <td>5.31</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>27.82</td>\n",
       "      <td>13.24</td>\n",
       "      <td>18.50</td>\n",
       "      <td>80.06</td>\n",
       "      <td>31.90</td>\n",
       "      <td>72.54</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3.80</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>32.5</td>\n",
       "      <td>5.05</td>\n",
       "      <td>104.51</td>\n",
       "      <td>11.1</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.250</td>\n",
       "      <td>258.0</td>\n",
       "      <td>5.31</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>20.80</td>\n",
       "      <td>7.08</td>\n",
       "      <td>26.15</td>\n",
       "      <td>75.05</td>\n",
       "      <td>31.05</td>\n",
       "      <td>67.68</td>\n",
       "      <td>59.1</td>\n",
       "      <td>5.04</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>32.5</td>\n",
       "      <td>4.43</td>\n",
       "      <td>66.97</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.203</td>\n",
       "      <td>255.0</td>\n",
       "      <td>5.31</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>26.08</td>\n",
       "      <td>21.45</td>\n",
       "      <td>23.90</td>\n",
       "      <td>76.72</td>\n",
       "      <td>30.86</td>\n",
       "      <td>84.82</td>\n",
       "      <td>52.9</td>\n",
       "      <td>3.34</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>30.8</td>\n",
       "      <td>4.32</td>\n",
       "      <td>77.19</td>\n",
       "      <td>13.7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.270</td>\n",
       "      <td>234.0</td>\n",
       "      <td>5.31</td>\n",
       "      <td>133.0</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7640 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     *r-谷氨酰基转换酶  *丙氨酸氨基转换酶  *天门冬氨酸氨基转换酶   *总蛋白   *球蛋白  *碱性磷酸酶  中性粒细胞%  \\\n",
       "0         20.23      23.10        24.96  76.88  27.28   99.59    54.1   \n",
       "1         79.00      36.25        24.57  79.43  31.67   67.21    52.0   \n",
       "2         38.17      15.23        20.82  86.23  38.23   63.69    48.1   \n",
       "3         20.22      10.59        14.99  70.98  26.96   74.08    41.7   \n",
       "4         22.72      14.78        20.07  78.05  36.22   75.79    56.6   \n",
       "..          ...        ...          ...    ...    ...     ...     ...   \n",
       "995       26.08      21.45        23.90  76.72  30.86   84.82    52.5   \n",
       "996       37.05      12.46        19.33  76.14  33.99  146.11    70.0   \n",
       "997       27.82      13.24        18.50  80.06  31.90   72.54    55.7   \n",
       "998       20.80       7.08        26.15  75.05  31.05   67.68    59.1   \n",
       "999       26.08      21.45        23.90  76.72  30.86   84.82    52.9   \n",
       "\n",
       "     低密度脂蛋白胆固醇  单核细胞%  嗜碱细胞%  ...  红细胞平均血红蛋白量  红细胞计数      肌酐  血小板体积分布宽度  \\\n",
       "0         2.65    6.5    0.6  ...        31.9   5.21   77.25       17.4   \n",
       "1         2.63    5.8    0.8  ...        29.9   5.21   87.12       10.3   \n",
       "2         2.01    7.7    0.8  ...        31.3   4.76   78.19       16.6   \n",
       "3         5.04    6.7    0.5  ...        31.9   4.29   61.46       10.8   \n",
       "4         3.65    9.1    0.6  ...        20.6   5.15   77.19       14.0   \n",
       "..         ...    ...    ...  ...         ...    ...     ...        ...   \n",
       "995       3.33    8.2    0.4  ...        28.9   5.47   95.72       13.2   \n",
       "996       3.51    7.0    0.6  ...        31.7   4.76   63.07       11.9   \n",
       "997       3.80   11.2    0.5  ...        32.5   5.05  104.51       11.1   \n",
       "998       5.04    5.7    0.6  ...        32.5   4.43   66.97       16.0   \n",
       "999       3.34    8.0    0.4  ...        30.8   4.32   77.19       13.7   \n",
       "\n",
       "     血小板平均体积  血小板比积  血小板计数    血糖   血红蛋白  高密度脂蛋白胆固醇  \n",
       "0        9.9  0.164  166.0  6.06  166.1       1.37  \n",
       "1        9.2  0.260  277.0  5.39  156.0       0.93  \n",
       "2        8.3  0.199  241.0  5.59  148.8       1.64  \n",
       "3       10.3  0.260  252.0  4.30  137.0       1.43  \n",
       "4       11.1  0.350  316.0  5.42  106.0       1.27  \n",
       "..       ...    ...    ...   ...    ...        ...  \n",
       "995     10.6  0.220  208.0  5.31  158.0       1.44  \n",
       "996     10.4  0.290  276.0  5.31  151.0       1.79  \n",
       "997      9.9  0.250  258.0  5.31  164.0       1.38  \n",
       "998      7.9  0.203  255.0  5.31  144.0       1.44  \n",
       "999     11.5  0.270  234.0  5.31  133.0       1.36  \n",
       "\n",
       "[7640 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>*r-谷氨酰基转换酶</th>\n",
       "      <th>*丙氨酸氨基转换酶</th>\n",
       "      <th>*天门冬氨酸氨基转换酶</th>\n",
       "      <th>*总蛋白</th>\n",
       "      <th>*球蛋白</th>\n",
       "      <th>*碱性磷酸酶</th>\n",
       "      <th>中性粒细胞%</th>\n",
       "      <th>低密度脂蛋白胆固醇</th>\n",
       "      <th>单核细胞%</th>\n",
       "      <th>嗜碱细胞%</th>\n",
       "      <th>...</th>\n",
       "      <th>红细胞平均血红蛋白量</th>\n",
       "      <th>红细胞计数</th>\n",
       "      <th>肌酐</th>\n",
       "      <th>血小板体积分布宽度</th>\n",
       "      <th>血小板平均体积</th>\n",
       "      <th>血小板比积</th>\n",
       "      <th>血小板计数</th>\n",
       "      <th>血糖</th>\n",
       "      <th>血红蛋白</th>\n",
       "      <th>高密度脂蛋白胆固醇</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5642</th>\n",
       "      <td>34.36</td>\n",
       "      <td>26.69</td>\n",
       "      <td>23.85</td>\n",
       "      <td>82.75</td>\n",
       "      <td>36.72</td>\n",
       "      <td>116.08</td>\n",
       "      <td>58.4</td>\n",
       "      <td>3.01</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>30.6</td>\n",
       "      <td>4.94</td>\n",
       "      <td>65.870</td>\n",
       "      <td>12.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.260</td>\n",
       "      <td>241.0</td>\n",
       "      <td>5.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5643</th>\n",
       "      <td>111.43</td>\n",
       "      <td>34.98</td>\n",
       "      <td>29.75</td>\n",
       "      <td>71.90</td>\n",
       "      <td>27.81</td>\n",
       "      <td>90.07</td>\n",
       "      <td>59.3</td>\n",
       "      <td>3.62</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>29.3</td>\n",
       "      <td>5.84</td>\n",
       "      <td>89.990</td>\n",
       "      <td>14.6</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.280</td>\n",
       "      <td>242.0</td>\n",
       "      <td>5.31</td>\n",
       "      <td>171.0</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5644</th>\n",
       "      <td>25.68</td>\n",
       "      <td>21.15</td>\n",
       "      <td>23.96</td>\n",
       "      <td>77.05</td>\n",
       "      <td>31.03</td>\n",
       "      <td>85.74</td>\n",
       "      <td>50.2</td>\n",
       "      <td>3.41</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>29.9</td>\n",
       "      <td>5.72</td>\n",
       "      <td>73.290</td>\n",
       "      <td>9.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.350</td>\n",
       "      <td>398.0</td>\n",
       "      <td>5.05</td>\n",
       "      <td>171.0</td>\n",
       "      <td>1.40</td>\n",
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       "    <tr>\n",
       "      <th>5645</th>\n",
       "      <td>23.41</td>\n",
       "      <td>16.63</td>\n",
       "      <td>17.98</td>\n",
       "      <td>78.16</td>\n",
       "      <td>32.72</td>\n",
       "      <td>95.95</td>\n",
       "      <td>53.7</td>\n",
       "      <td>3.87</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>29.9</td>\n",
       "      <td>4.61</td>\n",
       "      <td>70.820</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.290</td>\n",
       "      <td>247.0</td>\n",
       "      <td>7.23</td>\n",
       "      <td>138.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5646</th>\n",
       "      <td>15.70</td>\n",
       "      <td>19.80</td>\n",
       "      <td>19.12</td>\n",
       "      <td>80.76</td>\n",
       "      <td>33.86</td>\n",
       "      <td>76.97</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.45</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>31.8</td>\n",
       "      <td>4.62</td>\n",
       "      <td>73.910</td>\n",
       "      <td>11.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.350</td>\n",
       "      <td>335.0</td>\n",
       "      <td>5.93</td>\n",
       "      <td>147.0</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>25.68</td>\n",
       "      <td>21.15</td>\n",
       "      <td>23.96</td>\n",
       "      <td>77.05</td>\n",
       "      <td>31.03</td>\n",
       "      <td>85.74</td>\n",
       "      <td>52.5</td>\n",
       "      <td>3.33</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>28.9</td>\n",
       "      <td>5.47</td>\n",
       "      <td>95.720</td>\n",
       "      <td>13.2</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.220</td>\n",
       "      <td>208.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>37.05</td>\n",
       "      <td>12.46</td>\n",
       "      <td>19.33</td>\n",
       "      <td>76.14</td>\n",
       "      <td>33.99</td>\n",
       "      <td>146.11</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.51</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>31.7</td>\n",
       "      <td>4.76</td>\n",
       "      <td>63.070</td>\n",
       "      <td>11.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.290</td>\n",
       "      <td>276.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>27.82</td>\n",
       "      <td>13.24</td>\n",
       "      <td>18.50</td>\n",
       "      <td>80.06</td>\n",
       "      <td>31.90</td>\n",
       "      <td>72.54</td>\n",
       "      <td>55.7</td>\n",
       "      <td>3.80</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>32.5</td>\n",
       "      <td>5.05</td>\n",
       "      <td>104.510</td>\n",
       "      <td>11.1</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.250</td>\n",
       "      <td>258.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>20.80</td>\n",
       "      <td>7.08</td>\n",
       "      <td>26.15</td>\n",
       "      <td>75.05</td>\n",
       "      <td>31.05</td>\n",
       "      <td>67.68</td>\n",
       "      <td>59.1</td>\n",
       "      <td>5.04</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>32.5</td>\n",
       "      <td>4.43</td>\n",
       "      <td>66.970</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.203</td>\n",
       "      <td>255.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>25.68</td>\n",
       "      <td>21.15</td>\n",
       "      <td>23.96</td>\n",
       "      <td>77.05</td>\n",
       "      <td>31.03</td>\n",
       "      <td>85.74</td>\n",
       "      <td>52.9</td>\n",
       "      <td>3.41</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>30.8</td>\n",
       "      <td>4.32</td>\n",
       "      <td>74.805</td>\n",
       "      <td>13.7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.270</td>\n",
       "      <td>234.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>133.0</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      *r-谷氨酰基转换酶  *丙氨酸氨基转换酶  *天门冬氨酸氨基转换酶   *总蛋白   *球蛋白  *碱性磷酸酶  中性粒细胞%  \\\n",
       "5642       34.36      26.69        23.85  82.75  36.72  116.08    58.4   \n",
       "5643      111.43      34.98        29.75  71.90  27.81   90.07    59.3   \n",
       "5644       25.68      21.15        23.96  77.05  31.03   85.74    50.2   \n",
       "5645       23.41      16.63        17.98  78.16  32.72   95.95    53.7   \n",
       "5646       15.70      19.80        19.12  80.76  33.86   76.97    52.0   \n",
       "...          ...        ...          ...    ...    ...     ...     ...   \n",
       "995        25.68      21.15        23.96  77.05  31.03   85.74    52.5   \n",
       "996        37.05      12.46        19.33  76.14  33.99  146.11    70.0   \n",
       "997        27.82      13.24        18.50  80.06  31.90   72.54    55.7   \n",
       "998        20.80       7.08        26.15  75.05  31.05   67.68    59.1   \n",
       "999        25.68      21.15        23.96  77.05  31.03   85.74    52.9   \n",
       "\n",
       "      低密度脂蛋白胆固醇  单核细胞%  嗜碱细胞%  ...  红细胞平均血红蛋白量  红细胞计数       肌酐  血小板体积分布宽度  \\\n",
       "5642       3.01    7.5    0.3  ...        30.6   4.94   65.870       12.8   \n",
       "5643       3.62    7.7    0.5  ...        29.3   5.84   89.990       14.6   \n",
       "5644       3.41    7.9    0.6  ...        29.9   5.72   73.290        9.9   \n",
       "5645       3.87    7.2    0.4  ...        29.9   4.61   70.820       13.9   \n",
       "5646       3.45    8.0    0.3  ...        31.8   4.62   73.910       11.9   \n",
       "...         ...    ...    ...  ...         ...    ...      ...        ...   \n",
       "995        3.33    8.2    0.4  ...        28.9   5.47   95.720       13.2   \n",
       "996        3.51    7.0    0.6  ...        31.7   4.76   63.070       11.9   \n",
       "997        3.80   11.2    0.5  ...        32.5   5.05  104.510       11.1   \n",
       "998        5.04    5.7    0.6  ...        32.5   4.43   66.970       16.0   \n",
       "999        3.41    8.0    0.4  ...        30.8   4.32   74.805       13.7   \n",
       "\n",
       "      血小板平均体积  血小板比积  血小板计数    血糖   血红蛋白  高密度脂蛋白胆固醇  \n",
       "5642     10.8  0.260  241.0  5.50  151.0       1.44  \n",
       "5643     11.5  0.280  242.0  5.31  171.0       1.22  \n",
       "5644      8.9  0.350  398.0  5.05  171.0       1.40  \n",
       "5645     11.6  0.290  247.0  7.23  138.0       1.38  \n",
       "5646     10.4  0.350  335.0  5.93  147.0       2.25  \n",
       "...       ...    ...    ...   ...    ...        ...  \n",
       "995      10.6  0.220  208.0  5.40  158.0       1.44  \n",
       "996      10.4  0.290  276.0  5.40  151.0       1.79  \n",
       "997       9.9  0.250  258.0  5.40  164.0       1.38  \n",
       "998       7.9  0.203  255.0  5.40  144.0       1.44  \n",
       "999      11.5  0.270  234.0  5.40  133.0       1.40  \n",
       "\n",
       "[2000 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_feat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练...\n",
      "开始CV 5折训练...\n",
      "第0次训练\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=8 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=80, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=80\n",
      "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=0.2, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=0.2\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n",
      "第1次训练\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=8 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=80, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=80\n",
      "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=0.2, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=0.2\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n",
      "第2次训练\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=8 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=80, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=80\n",
      "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=0.2, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=0.2\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n",
      "第3次训练\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=8 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=80, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=80\n",
      "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=0.2, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=0.2\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n",
      "第4次训练\n",
      "[LightGBM] [Warning] num_threads is set with n_jobs=-1, nthread=8 will be ignored. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=80, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=80\n",
      "[LightGBM] [Warning] min_sum_hessian_in_leaf is set=0.2, min_child_weight=0.001 will be ignored. Current value: min_sum_hessian_in_leaf=0.2\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    }
   ],
   "source": [
    "# !pip install lightgbm -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
    "\n",
    "import  pandas as pd \n",
    "import numpy as np \n",
    "from sklearn.model_selection import KFold  # K折交叉验证函数\n",
    "import lightgbm as lgb  # LightGBM是一个梯度Boosting框架，使用基于决策树的学习算法\n",
    "\n",
    "# 读取处理后的数据\n",
    "train_feat = pd.read_csv('../tmp/train_feat.csv', encoding = 'gbk')\n",
    "test_feat = pd.read_csv('../tmp/test_feat.csv', encoding = 'gbk')\n",
    "predictors = [f for f in train_feat.columns if f not in ['血糖']]  # 提取列名\n",
    "print('开始训练...')\n",
    "# 训练模型并预测\n",
    "def lgb_train(train_feat,test_feat):\n",
    "    print('开始CV 5折训练...')\n",
    "    train_preds = np.zeros(train_feat.shape[0])\n",
    "    test_preds = np.zeros((test_feat.shape[0],5))\n",
    "    kf = KFold(n_splits=5, shuffle=True, random_state=500)  # K折交叉验证\n",
    "    for i ,(train_index, test_index) in enumerate(kf.split(train_feat)):\n",
    "        print('第{}次训练'.format(i))\n",
    "        train_feat1 = train_feat.iloc[train_index]\n",
    "        train_feat2 = train_feat.iloc[test_index]\n",
    "        model_lgb = lgb.LGBMRegressor(objective='regression', num_leaves=8,\n",
    "                                      learning_rate=0.03, n_estimators=400,\n",
    "                                      max_bin=30, bagging_fraction=0.8,\n",
    "                                      bagging_freq=10, feature_fraction=0.5,\n",
    "                                      feature_fraction_seed=10, bagging_seed=10,\n",
    "                                      min_data_in_leaf=80, nthread=8,\n",
    "                                      min_sum_hessian_in_leaf=0.2)\n",
    "        gbm = model_lgb.fit(train_feat1[predictors].values, train_feat1['血糖'].values)\n",
    "        train_preds[test_index] += gbm.predict(train_feat2[predictors])\n",
    "        test_preds[:,i] = gbm.predict(test_feat[predictors])\n",
    "    return train_preds, test_preds\n",
    "train_preds,test_preds = lgb_train(train_feat, test_feat)\n",
    "\n",
    "# 保存数据\n",
    "train_true_pred = pd.DataFrame({'true':train_feat['血糖'], 'pred':train_preds})\n",
    "train_true_pred.to_csv('../tmp/train_true_pred.csv', index=False, encoding = 'gbk')\n",
    "test_preds1 = pd.DataFrame(test_preds.mean(axis=1))\n",
    "test_preds1.columns = ['血糖']\n",
    "test_preds1.to_csv('../tmp/test_preds.csv', index=False, encoding = 'gbk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 性能度量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H/8/+15iWcTtB4hs56RfGIlpr+ZAP+QjxgWlP/SLo6zWYN96HgRP7HPsnzDb7RUHXfQtT0bg6xGPbMVlPGcBZYZ7/B/QY4m8ECv3X/xDT/jsbIxRu8F+/FLOjLh3TyvsjJnsIjAB7GRMKCabK9RKQ4f/6S8B9mN1mD/iv248xEFsf64LW9n+YVthXMdlXweteiMnx+of/a+X/+dRghOFpfY4vx4iG1/pcv93/fb/TZx3Wx9vAVVF+h/di2lgn9Ln+MYxvqRxTpXnIv7Y8/+3zMW3RqRhBdan/+lcwOyPBZHv9GSMwNgx0/f41nA3kB933HOCeoK+nAW8FfX0mYAv6+nrMJobg558V9Pm1QIr/88OD/g5KMNXLbwHZQcff4v97qcAI7OC/67uAa4O+TsLEfFhfX4ARUxf5v57k/1m2AlOD/pc2AmsjfBwY6dcA+ZCPeD+U1tGqw4IgREOZHYCdWuuQ6dcJek4bgNa67y64oXr8NG18WKFuS9KmTRTp/nZtWlUoM0rHqU2yeLjjc7TWLYNa9ABRSs3WWm/qc12R1rquz3WOUL9jpVS67p+aP5j1pACpI/XzGCxKqWQd5DP0VyGztdb1/q8LgGbr70MQDhZEVAmCIAiCIAwB4qkSBEEQBEEYAkRUCYIgCIIgDAEiqgRBEARBEIaAEd+yWlhYqCdNmjTSyxAEQRAEQYjK+++/X6+1Lgp124iLqkmTJrF69eroBwqCIAiCIIwwSqlw0wWk/ScIgiAIgjAUiKgSBEEQBEEYAkRUCYIgCIIgDAEj7qkSBEEQBGF04fF4qKqqorOzc6SXMmKkpqZSXl6Ow9F3rGd4RFQJgiAIgtCLqqoqsrKymDRpEkqpkV7OsKO1pqGhgaqqKiZPnhzz/aT9JwiCIAhCLzo7OykoKDgkBRWAUoqCgoK4K3UiqgRBEARB6MdoE1Rer5do84r37duH2+0Oe3uwSPJ4PHg8/eajBxjI9y/tP0EQBEEQRh3d3d289dZb2O12AB544AHS09M577zzALDZbCxdurTXfb73ve9x2mmn8fGPfzzkY1544YV897vfpaKighdffJHly5fz05/+FK/XS0VFBUlJg5NFQy6qlFKTgb8C2cB7WutvDPVzCIIgCIJwcKO1pqamhpSUFAC2bt3KeeedR319PWBEFcDMmTOZMGECAB0dHWzcuJF//vOfAFRWVrJ9+3YAduzYQUpKCl1dXTz00EOsWrWKrq4uHn74Ybq7u7n++uvJysoa1JoTUan6NfBTrfVKpdQDSqkTtdavJ+B5BEEQBEE4SHE4HFx++eUsWrQoIHYefvhhABoaGli3bh0ASUlJPPjgg2zdupUlS5bw4IMPMnv2bA477DAWLlwYeLybbrqJ2bNnc+qpp/LLX/6SqqoqbDYbLS0t/OAHPxi0oILEiKoZwBr/57VATgKeQxAEQRCEYeDHT21g477WIX3MOaXZ/Oi8uTEd63A4OPHEE3td9/TTTwc+f+6559i/fz+PPPIITz/9NK2trRQXFwOwZo2RIw899BAffvghkydPxmaz4XQ6uffeewF45plnaGpqGoLvKjGi6mHgR0qplcCZwHcT8ByCMLbp7oLWasifMtIrEQRBGPWsWLGi19fBhnWn08nPfvYzrrzySs477zwuu+wyZsyY0ev4uXPncsstt/D6668Dpk1otQVra2uHbJ1DLqq01j9TSi0DbgT+pbVu73uMUuo64DqAiRMnDvUSBGH08/6/4OUfwbd2gSN1pFcjCIIQllgrSolk8eLFvb5++eWXAXjqqaf4zW9+Q0ZGBn/961+59dZbWbVqFZ/+9KcBI7h+8YtfcNxxx+FyuQL3379/P3feeScANTU1nHbaaUOyzkTt/lsLTASuDHWj1voO4A6AxYsXR94fKQgHI+014HFBV6uIKkEQhBDcf//9/OMf/2DHjh1kZmb2um337t2cdNJJfOITn2D58uWAqV7ddNNNnHjiiYwfP55PfvKTYdPQCwsLufDCCwF47733hmzNicqpuhH4g9baFfVIQTgUcTvNZefQ+hQEQRAOFj7+8Y/z2muv8eijjzJ+/HgeffRRXnrpJaZOncp3vvMdHnnkEa699lqqq6v55z//ybJly8jKyuKGG26gurqapUuX8sgjjwQeT2uNz+fD6/WSk5PDsmXLWLZsWaBV6PV6B73mhFSqtNY/SsTjCsJBgyWqukRUCYIghEJrzVe/+lUKCgr44x//SHZ2NgB/+9vfuO+++/ja177GHXfcwU9/+lNmzZrFI488EjCof//73+dzn/scf/zjHzn77LNJS0ujq6uL+vp6zj77bIqKirj55psDz7Vq1Sq6u7u54oorBrVmFS2dNNEsXrxYr169ekTXIAjDzkPXwIZH4eonYMqJI70aQRCEXmzatInZs2eP9DJGnFA/B6XU+1rrxaGOlzE1gjASePyd8a62kV2HIAiCMGSIqBKEkUA8VYIgCAcdIqoEYSQIeKqkUiUIgnCwIKJKEEYCEVWCIAgHHSKqBGEkCHiqWkZ2HYIgCGMEj8eDz+cLfN3d3Y3P56OtLfzJ6c6dO3uNoOns7Oz1eB6PZ0jXKKJKEEYCt3/QgFSqBEEQQrJ8+XJOO+00zjvvPMrKyrjrrru44IILKCgo4MILL+TCCy/k7bff5tRTT+X111/nYx/7GJ/+9Ke5/PLL+eCDDwD45z//Gfgc4MILL+SNN96gsrKSu+++m8985jNUVlayY8cOuru7B71mEVWCMBK4/ZUqMaoLgiCE5LjjjuPb3/42M2fO5K9//Stf+MIXeOqpp1i8eDGPP/44Tz/9NMuWLePZZ5+lvLwcu93Oz3/+cxYtWkR9fT2nn346SUlJ2GxG6uzYsYOUlBS6urp46KGHePnll2ltbeXhhx/moYceoqOjY9BrFlElCMONtxu8XeZzqVQJgiCEJT09nXfffZeLLrqIlStXctRRR1FRUcEXvvAF5s+fz8qVK3nvvfeYNm0aAF/4whdobm7G4XCQnJzc67FuuukmZs+ezamnnsqzzz7LBx98wObNm3n66ac58sgjycrKGvR6EzX7TxCEcHicPZ9LorogCKOd574DNeuG9jGLD4OzfhXxkPvuu4877rgDrTUnnngiZ555JmeffTaZmZkcc8wxVFVVMXfuXC6++OKAqLLb7YHk9WAeeughPvzwQyZPnozNZsPpdHLvvfcC8Mwzz/TyXQ0GqVQJwnDjDhZVUqkSBEEIxZVXXsnrr79Obm4uRx11FKWlpQDU1NRQWFgIQFZWFk8++SSTJk1Ca43H4yEpKYm+02Lmzp3LLbfcEvi6o6OD7du3s337dmpra4dszVKpEoThxvJT2RziqRIEYfQTpaKUKCwvFMAvf/lL1qxZw86dO9m7dy8TJ04MCKcnnniCrVu3kpSURFtbG1lZWQGBZTFnzhxcLlfg6/3793PnnXcCRqSddtppQ7JmEVWCMNxYO/+yikVUCYIgxIDdbic9PZ2KigrefPNNUlJSOOaYY/D5fPz5z3/m4YcfZu3atTz66KPs2rWLz33uc/h8PlasWBHy8QoLC7nwwgsBeO+994ZsndL+E4Thxsqoyio2nqoRHmouCIIwWtFaBypSc+fOpa2tjVNOOYWrrrqKU045hTvvvJOlS5eSlZXFD3/4Q26++WZSU1O57bbbmDlzJps3b8Zutwcey+fz4fV6ycnJYdmyZSxbtowZM2YA4PV6B71eqVQJwnBjeaqyigFtKlcpg991IgiCcDDhdrtZunQpV155JV6vlxtuuAGfz8ett96Ky+Xi8ssv5+abb+ayyy6jtraWH/3oR5SXl3PTTTexf/9+Dhw4wMKFCznqqKMA6Orqor6+nrPPPpuioiJuvvnmwHOtWrWK7u5urrjiikGtWUSVIAw3AVFVYi672kRUCYIg9CE5OZnVq1cHvv7+979PWVkZYAzqDz/8MCkpKdjtdnJzc5k6dWrgfhUVFVRUVLB48eLA/a3KVCKR9p8gDDe9KlWIr0oQhFFJ3x10I40lqCzS09MDrb1EMJDvX0SVIAw3AU9VUKVKEARhFJGamkpDQ8OoE1bDhdaahoYGUlNT47qftP8EYbgJ3v0HMlRZEIRRR3l5OVVVVdTV1Y30UkaM1NRUysvL47qPiCpBGG7cLkBB5njztVSqBEEYZTgcDiZPnjzSyxhzSPtPEIYbtxMc6ZCaY74WT5UgCMJBgYgqQRhuPE5IzujZ8SeVKkEQhIMCEVWCMNy4nZCcDsmZ5msRVYIgCAcFIqoEYbhxu4ygstkhOcukqguCIAhjHhFVgjDcuNuNpwpMC1BElSAIwkGBiCpBGG48LuOpAkjNFqO6IAjCQYKIKkEYbtzOHlGVkiWeKkEQhIMEEVWCMNz0ElXZ0v4TBEE4SBBRJQjDjZVTBVKpEgRBOIgQUSUIw414qgRBEA5KRFQJwnDi8/UWVSnZUqkSBEE4SBBRJQjDicdlLoNFlccJ3u6RW5MgCIIwJIioEoThxO00l8GeKgC3VKsEQRDGOiKqBGE48fhFlTWiRub/CYIgHDSIqBKE4cSqVCX7K1Wp2eZSzOqCIAhjHhFVgjCcuPt6qqRSJQiCcLAgokoQhhN3u7l0WKIqx1xKAKggCMKYR0SVIAwn/Xb/SaVKEAThYEFElSAMJwFPVVD4J0Bny8isRxAEQRgyRFQJwnDSV1RJpUoQBOGgQUSVIAwnfUWVIx2UXTxVgiAIBwEiqgRhOLE8VVb4p1IyVFkQBOEgQUSVIAwn7nZISgObvee6VJn/JwiCcDAgokoQhhO3qyf40yIlW8I/BUEQDgJEVAnCcOJ29vipLFKyxFMlCIJwECCiShCGE4+zJ/jTIiVbRJUgCMJBgIgqQRhOwlaqxFMlCIIw1hFRJQjDSShPVap4qgRBEA4GRFQJwnDidkJyZu/rpFIlCIJwUCCiShCGE4+zJ6PKIiUbvF3Q3TUyaxIEQRCGBBFVgjCchPRU+ef/SbVKEARhTCOiShCGE7erv6iyhirLDkBBEIQxjYgqQRgutDaJ6qF2/4GY1QVBEMY4IqoEYbjo7gR0CE+VX1RJ+08QBGFMI6JKEIYLt9Nc9tv9J+0/QRCEgwERVYIwXARElVSqBEEQDkYSIqqUUnlKqWeVUquVUrcn4jkEYcwREFV9jeo55lI8VYIgCGOaRFWqrgLu01ovBrKUUosT9DyCMHbwuMxlv9l/VqVKRJUgCMJYJlGiqgGYp5TKBSYAexP0PIIwdnC3m8u+laqkFLCniKgSBEEY4yRKVK0AKoAvA5uAxuAblVLX+VuDq+vq6hK0BEEYZbj9laq+niqQUTWCIAgHAYkSVT8CvqC1/gmwGbgm+Eat9R1a68Va68VFRUUJWoIgjDLC7f4DGaosCIJwEJAoUZUHHKaUsgNHAzpBzyMIYwePX1T1zakCqVQJgiAcBCRKVP0SuANoAfKB/yboeQRh7BBu9x+YrCoRVYIgCGOapEQ8qNb6PWBuIh5bEMYsAU9VGFHVvHt41yMIgiAMKRL+KQjDhbsd7Mlgd/S/LSVLdv8JgiCMcURUCcJw4XGF9lOBGNUFQRAOAkRUCcJw4XaG3vkHPUZ1LXs6BEEQxioiqgRhuHA7Q2dUgfFUaW9P6rogCIIw5hBRJQjDhdsZ2qQOMlRZEAThIEBElSAMFx5X/7l/FjJUWRAEYcwjokoQhgt3u1SqBEEQDmJEVAnCcOF2RfZUgcQqCIIgjGFEVAnCcBGTp0pElSAIwlhFRJUgDBceZwRPlVWpkvafIAjCWEVElSAMF7FUqsSoLgiCMGYRUSUIw0G3G3zd4T1VyWJUFwRBGOuIqBKE4cDdbi7DJarbk0xrUDxVgiAIYxYRVYIwHFhJ6eFm/4EMVRYEQRjjiKgShOHA7TSX4TxVIEOVBUEQxjgiqgRhOIhFVFlDlQVBEIQxiYgqQRgOYhJV2SKqBEEQxjAiqgRhOAh4qqJVqqT9JwiCMFYRUSUIw0Fg918UT5VUqgRBEMYsIqoEYThw+ytV4XKqwLT/xKguCIIwZhFRJQjDQcBTFSanCoyocreBzzc8axIEQRCGFBFVgjAcePyiKlpOFRhhJQiCIIw5RFQJwnDgdoKyQ1JK+GNSZFSNIAjCWEZElSAMB26XMakrFf6Y1GxzKb4qQRCEMYmIKkEYDtztkXf+gVSqBEEQxjgiqgRhOPC4IvupAFJyzKVkVQmCIIxJRFQJwnDgdsZRqRJRJQiCMBYRUSUIw0EsosryVEn7TxAEYUwiokoQhoN4KlViVBcEQRiTiKgShOHA7YzuqUrOBJRUqgRBEMYoIqoEYTjwuCKnqYOJW0jJFk+VIAjCGEVElSAMB+72yHP/LGSosiAIwphFRJUgDAdW+Gc0UrKgsyXx6xEEQRCGHBFVgpBovN3g7QJHjKJKKlWCIAhjEhFVgpBorGHKMVWqxFMlCIIwVhFRJQiJxm2Jqhg8VVKpEgRBGLOIqBKERON2mctou/9AjOqCIAhjGBFVgpBo3O3mMlpOFfiN6tL+EwRBGIuIqBKEROOxKlWxeKpyoLsDvJ7ErkkQBEEYckRUCUKiccdjVLeGKksLUBAEYawhokoQEk08oiowVFlagIIgCGMNEVWCkGgsURWrpwrEVyUIgjAGEVElCInGE8fuP2n/CYIgjFlEVAlCorF2/8WUUyXtP0EQhLGKiCpBSDRuF6AgKS36sQFRJZUqQRCEsYaIKkFING6n8VPZYvh3E6O6IAjCmEVElSAkGo8ztp1/IEZ1QRCEMYyIKkFINO44RFVSKtgc0v4TBEEYg4ioEoRE43bFLqqU8g9VlkqVIAjCWENElSAkGnd77KIKZKiyIAjCGEVElSAkGo8rtuBPCxmqLAiCMCYRUSUIiSYeTxWYocpSqRJGgse+CCv+ONKrEIQxi4gqQUg0IUTV+7sb+doDa/H5dP/jU7Kgq2WYFicIflyN8OF/YefrI70SQRiziKgShEQTQlS9trmOxz6opqXD0//4lCypVAnDz45XAQ3OhpFeiSCMWURUCUKiCeGpanK5AUKLqtRs8VQJw8+2l8yls25k1yEIY5ikRDyoUuqLwOX+L3OBd7XWn0/EcwnCqMbnM6KqzzDliKLKqlRpbSIWBCHR+Hyw/WXzuate/vYEYYAkpFKltf671vpErfWJwHLgH4l4HkEY9Xhc5rLPMOUmpxFTzSFFVTb4PNDdlejVCYJh/1ojpkoWgK8bOptHeEGCMDZJaPtPKVUGjNdar07k8wjCqMXtNJd9PFVRK1UgAaDC8LHtJUDB/MvM1+KrEoQBkWhP1fXA3/teqZS6Tim1Wim1uq5O+vfCQYzHL6occYiq1BxzKWZ1YbjY/hKUHgFFs8zX4qsShAGRMFGllLIBJwGv971Na32H1nqx1npxUVFRopYgCCNPiEqV1jrQ/mvxi6teBIYqS6yCMAy4GqFqNUw/HTIK/dfVj+yaBGGMkshK1XEYg3qIIB5BOERw9/dUudxe3F4fEK79l20upVIlDAdWlML00yDDf5IrlSpBGBAJ2f3n5wzgzQQ+viCMftzt5jJo91+js6c6JZ4qYcTZ9hKk5Zv2n6/bXCeeKkEYEAkTVVrrmxL12IIwZrB2/wXlVDW7PCE/D5AqlSphmLCiFKadAja7+UjJkUqVIAwQCf8UhEQSwlNlmdQddhW5/ScBoEKisaIUpp3Wc11GgXiqBGGAiKgShEQSQVRNyE+P0v6TSpWQYLa/DChTqbLIKJJKlSAMEBFVgpBIQokqv6dqckFGaFFld0BSmniqhMSz7UXjpbJ2/QGkF4qnShAGiIgqQUgkITxVjS4PSsHEgjCVKvCPqhFRJSSQ4CiFYDIKpf0nCANERJUgJBJ3u6k62eyBq5pdbnLSHOSnJ5t4hW5f//ulZkv7T0gswVEKwWQUgrPemNgFQYgLEVWCkEjcrn5z/xqdbvLTk8lJdwARYhXEqC4kkuAohWDSC0F7Zf6fIAwAEVWCkEjczn5z/5pdHnLTHeSkRRJVUqkSEkjfKIVgAgGg0gIUhHgRUSUIicTj7Df3r9HpJi89OUhUhRlVI54qIVGEilKwyCgwl+KrEoS4EVElCIkkZKXKTV5GcuRKVWqOVKqExBEqSsFCRtUIwoARUSUIiSSEp6rJ5SEvavtPPFVCAtn2Uv8oBYt0/3XS/hOEuIkoqpRSaUqpE/2fn6GUyh+ORQnCQYPb2WvuX6fHS4fHS15GMrnpyUCYUTUp2ab9J/PIhaHG1QhVq/pHKVik+9t/IqqEUUBtaydLf/kK66tbRnopMRGtUpUKnKWUuh/4M/C0UurzSqlnlVLblVLPJX6JgjCG8Th7ZVRZaep56clkp5rRm+FT1XVPeKggDBXhohQskpJN+1k8VcIo4MOqFva1dPLmtrHRjo42UHkBkAsUAO8C24CFwDNAktb6rEQuThDGPH08VY3OHlGVZLeRmZIUZVRNK6Rk9r9dEAZKuCiFYGRUjTBK2N1gTiw3VI8NO0Q0UfVZoBvIBjqAcUA+ID0JQYgFt6uXqLJafXn+jKqcNActodp/qf6hymJWF4aSSFEKwaQXSvtPGBVU+kXV+n0HR/vvemA/cDuwBvgfcBtwBNCplLonoasThLGM1iZRPUSlKj/D+Kly0hzhc6pAzOrC0BIpSiGYjEJwyfw/YeTZ3eAKXIYd6zWKiFap6gYWAZOBNIyYSga6gP9orR9M7PIEYQzT3QnoXp6qZr+nyjKpRxVVklUlDCWRohSCySiEve8Oy5IEIRKVDU4KM5Opb3ezcV8rx0wtGOklRSRapcoL5AC7gAPAz4B04JvASUqpRxO7PEEYw1gm86Ddf41OI6By/e2/3HQHzdE8VYIwVESKUggm3V+pkvl/wgji7vZR3dTBGXOLAdgwBlqA0UTVXcBGYCZwEvADoB74PmZnYFpCVycIY5mAqOq9+y8rNQmH3fzrha1UiadKGGpcjVC9OnyUQjAZRaB90NGU+HUJQhj2NrnwaVhUkUdJTuqYiFWI1v77OnAUMAEjwN4EPgX8DngWcCR0dYIwlgmIqmCjuhlRY5GTbkSV1hqlVM99rUqVeKqEoWLHq0YohYtSCMaqZLnqe8bWCMIwY+38qyjIYG5pDuv3jf7Xw4iVKq31AaAUeAl4WGv9B2AJUAg0AtckfIWCMFbxGINl8Oy/RpeHvIwgUZXmwN3to9PTp82SbLX/pFIlDBGxRClYWKJKYhWEEaSy3ryGTi7MYF5ZNjvq2nG5u0d4VZGJOqZGa307cAJgV0pdDXwMaACuBk5N7PIEYQzjbjeX/SpVPQXesKNqbDYjrOIQVQ3tXTQ5QwxnFoRYoxQsZFSNMAqobHCSlZpEXrqDeaU5aA2b9o/ualWss/++BngwxnUvZldgt/9zQRBC4fZXqoI8VY1ON/lB7b/cNP+omo4QYiglC7pi9xB89YG13PjwRwNbq3BwE2uUgoUMVRZGAZUNLiYVZKCUYl5ZDgDrR3kIaDRPlYUGOgFFT/CnCn+4IAihdv81uzyBOAUIqlSFCwCNo1K1t9FFenKs/9LCIUWsUQoW6f4xr5JVJYwguxucHOYXU+OzUyjMTB71ZvVYK1V2IDPEhyAI4fD4RZU/p8rd7aO9q5v8jBjaf2AqVXEY1Rva3YEcLEHoRaxRChZ2B6TlSaVqMDjrwTv6wypHKx6vj6qmDiYVGPuEUmpMmNUjiiql1OVKqR/QeyyNVKgEIRb67P7rG/xpPjeiKnRWVeyVqk6Pl7au7tCPIxzaxBOlEIyMqhk43W74y0JYdedIr2TMUt3UgdenmVTY40mdV5bNtgNtdHpGr/MoWqVqBfARxjvVHuJDEIRwBDxV5kWh0dV7RA1Atr9S1RquUhVj+Kc1/sbl9tLVPXpfcIQRYP9aE6Uw6dj47pdRJKJqoLTtg84WqNsy0isZs+zyxylMKujxpM4rzaHbp9l6YPTuio4WqVCttX4CI6qagSb/R7P/QxCEcLjbwZ5sWilAU580dYCslCSUCtP+i8NT1dDe0/YL6c8SDl2sN/ai2fHdL6PAmNuF+GmpNpdtNSO7jjHM7vqejCqLsWBWj9XV+igmBFT3uT5WT5YgHHp4XCHn/gWHf9psipw0B82hhFBKdsyeqnpnV8/zdHgYl506wEULBx11m00+Vax+Kov0QnC+k5g1Hey0VJnLtv0ju44xTGWDi4xkO4WZPa+X5XlpZKcmsX4Uj6uJSVRprX/S9zqllAgqQYiE29l77l+I9h9EGarscYLPGzVbKLhSJVlVQi/qtkDRLFBx2mEzivzz/6L//Ql9aNlrLkVUDZjdDU4q/HEKFla0woZRvANwwMJIa+3TWv96KBcjCAcVbmevjCqrGhXc/oNIoir2ocqNfSpVggCA1ujaTexJmoC7O87hyBmFgJb5fwOh1d/+a68F7+hOAB+t7G5wMTnIpG4xryyHTTVteLyjc9i3VJsEIVG4nb3S1BudbjKS7aQk9T7rz0lzhBZCcQxVFk+VEBJnHaqzmX9uSeGZdfviu6+Mqhk4VvsPDc7aEV3KWKTb62NPo4uKIJO6xdzSbNzdPrbXjs69ciKqBCFReFy95v41udy94hQsctIc4Xf/QUyiqr7dTXZqUuB5BAEwfipgmy5jY7z5PjKqZuC0VIE9xXzeKi3AeNnX3Em3TwcyqoLpMauPzhagiCpBSBTu9l6Vqianu5+fCkw7MGL7LwazeoOzi4kF6TjsStp/Qg/+nX/bfOVsrolzG7qMqhk4LdVQusB8Lr6quKlssHb+9a9UTS7IICPZzoZRGgIqokoQEoXb1ctT1eTy9PNTQY+nSus+m2tTzBlZrO2/wswUctKSQ+8kFA5N6jbjVBnUksum/fGKKn+lSkbVxEdni5nZWX6k+VpEVdzstjKqQniqbDbFnNJsqVQJwiFHH09Vkyt0pSonzYHXp2nv6mNojcOo3tDeRUFGCnnpDhlVI/RQt4Xtupwkm4369i7q2rqi38ciLR9QUqmKFyujqmQBKLtkVQ2AXfUu0hx2xmWlhLx9bmkOG/e34vX1TXkaeURUCUKi8Dh7e6qc7l4ZVRa5aea6fi3AgFE9sqjSWlPvdFOYmUxuepjMK+GQxFe7iU3dJZwww7TytsTTArQn+ef/iacqLqydf3kVkDleKlUDwMQppPeKUwhmXlkOLreXXf6A0NGEiCpBSBRBlapur4/Wzu6Q7T9rVE0/MRSjp6q9qxt3t4+CzGTT/hNPlQDgbMDmqmebLuP8BaUAbK6J04eSUSSVqnixMqqyyyCrWETVAKhscIY0qVvMKzMnnBtGYQioiCpBSATdbvB1BzxVltAJ1/6DEPP/HOmmfRDFU2XN/cuX9p8QTL0xqW/X5SyZUsC4rBQ27o9XVBWKpypeWqrM/21WMWSVSPsvTrw+zd7GDioK+5vULaYVZZKSZBuVvioRVYKQCNz+DBV/oroldEJFKljVq37tP6ViGqpc78+oKpD2nxCMP06hJrmCcVkpzCrJZnO8ZvX0Amn/xUtLtalS2eyQXQKtceaDHeLsb+nA7fVFrFQl2W3MKskelTMARVQJQiLwuMylf/Zfo3+Ycn6YnCoIk4Qew1DlhnZjPi7MSCE3PZkOj5dOj3egKxfi4M7lO/n03e+N9DJCU7eFDpVGzvjJKKWYXZzF9tr2+JKopf0XPy1VkFNmPs8qhs5m8HSM6JLGEpX15rUzkqgCmFeazfp9Lf13TY8wIqoEIRG4/QZKv6eqKVCpCh2pACEqVWDm/0UTVc7elaqwjyUMKT6f5q4Vu3hjax1d3aNPxOq6zWzXpUwvNt68WSVZuL2++My9GYVmTI2MWomdlr2QU24+zyoxl9ICjJnKQJxC+PYfGLN6W2c3extHl2AVUSUIiaCvqHKGHqYMkJ5sN6Gdodp2Kdkm9yYCVqUqPyM5sJNQWoCJZ/XuJva3dKI1VDWNrhd2AN+BzWzxljHTL6pmlxhz76Z4fFUZRZj5f40JWOFBiM9n2n3ZVqVKRFW87G5wkpJkY3xWasTj5pX6k9VHmVldRJUgJIJ+lSojckJFKiilIg9VjlKpqm93k5WSRKrDHqhUiVk98TyxtjrwuRVWOGroaMburGGbr4wZ442omlKYicOu4ktWTy8wl+Krig1nLfg8ISpV4quKlcoGM/PPZgsdp2AxoziTJJsadWZ1EVWCkAgCnqqe9l+qw0Zasj3k4RHn/0Uxqjc43RRkGrFmiaomqVQlFI/Xx7Pr9rN0qhEduxtcI7yiPtRvBczMP0tUJSfZmFqUyea4K1WIrypWrEHKORPMZVaxuZRKVcyYjKrIfiqAlCQ7M8ZnsX6UjasRUSUIiSCw+6+n/ReqSmWRk+aguSNEdSlGo3pBpkketnYXtoR6LGHIWLG9niaXh2uOnUx6sn30iSr/zr+GtMm9Ws6zS7LjG1cTGFUjlaqYCIgqf/svLc8MVpasqpjw+TS7G1xMDjGeJhTzyrLZUD26zOoiqgQhEbj9b7L+nCoz9y+yqArb/osS/tkYNKg5N1yQqDCkPLl2HzlpDk6YUURFQcboa//VbaGLFLKKJ/e6elZxFjWtnQGPX1QClSoRVTEREFX+9p9SJlZBKlUxUdPaSVe3L+Qg5VDMK8uhwemmprUzwSuLHRFVgpAIAp4qk1Nl5v713/lnkZueHH73n7cLusPPbKtvNyNqwJjek+02af8lkA63lxc31HDWvGKSk2xU5Kezu3F0Vap07WZ26BKmF+f2un6W36wes68qLQ8z/09EVUy0Vpv/+dTcnuuySqBVKlWxENj5F0P7D8wMQGBU5VWJqBKERODxiyqHValyR61Uhd39B2FbgD6fptFphimD3/Se7pD2XwJ5dXMtTrc3MPqloiCdqsaOUTXc1Vu7iS2+Mmb6/VQWs0vM1zGPq7HZ/QGg4qmKiZa9Zudf8Mw6GVUTM1YbPdZK1eySLGyKUWVWF1ElCInA7TSjKpKM2GlyukMGf1pkpzlo6+zu/8YcZahyc4cHnyZgVAfTApT2X+J4Ym0147JSOHqyMalXFGTg9vpGTwuiq42ktmq2+cqY3kdUFWWmUJCRHF+yekaheKpipaWqp/VnkVVq2n+jyPczWqmsd5Jst1GSkxbT8enJSUwtyhxVMwBFVAlCInC7jEldKbw+TUuHh7wQwZ8WlheqrTPMUOUwlSoro8oyqoOJbWiSSIWE0NLh4fUtdZw7vxS7f8u3dVa9O55QzUTi3/m3XZcxY3xmr5uUUswqyYpvsHJGkbT/YqWlOoSoKjaV6yi7eAXT/ptYkB7434qFeWU50v4ThIMed3tg51+rv5qUFyL40yInnMHcav+FMatbc/8Kgx47R+b/JYwX1tfg9vq4wN/6A5iY7xdVo8VXVWcGKbdmTiUrtb+Qn1WczZYDbbG3K2X+X2x4Ok1OVT9RJQGgsbK7wcWkGFt/FnNLs6lp7aSuLbzvdDgRUSUIicDj6uWngtDBnxZhR9VEq1Q5+1eqcsPtJBQGzZMf7qOiIJ355TmB60pz03DY1eiJVajbjIckMoqnhbx5VnEWnR5fwBQcFWn/xUarPww2VKUKxFcVBa01lTFmVAUzr8z8L46WFqCIKkFIBG5nv7l/kSpVYWf2WZ6qMKNqGtp75v5Z5GVI+y8R1LZ18vaOes4/vBQVZES22xQT8tJHTayCr3YzO3Qp0/rs/LOwxtXE7KvKKPLP/xOhHpFwoirbX9WUSlVEatu66PT44q5UzSk1f88bRkkI6EEvqppdbu54cwfba+MwZgrCYAkWVU5rRE14T1Wg/ddXVGWVgLJB066Q92twulGqdxUsJ81Bp8dHp2f0Dfkdyzzz0X58ml6tP4uJBemjplLlPbCZbb7SQJJ6X6aNy8RuU7H7qqxRNa6xN//vhQ01w1e1tTKqrLl/FpnjzWWrjKqJhDXoO95KVXaqg0kF6aNmB+BBL6q6fZpfPLuZlzbWjvRShEOJUJWqgbT/HGmQPxUObAh5v4b2LvLSk3sZO8NWvYRB8eSH+5hdks20cf3FyqSCDPY0ukY+2dntIql1D9t85YFByn1JddiZUpgRe7L6GB1Vs6+5g8/f+z53Ld85PE8YTlSlZBpvpFSqImJVemNNUw9mblnOqBmsnFBRpZS6VSl1XiKfIxqFmSlMG5fJu7saRnIZwqFGKE9VhPZftl9UhZz/N2421G4Keb+GdjcFfR7XEm/SAhw69jS4+GBPc8gqFRizentXNw2xJpUnioZtKDTbKWPauMywh80qyY69UjVGR9VY3987O4fptb+lCjLGgSO1/21ZJeKpikJlgwuHXVGSE+LnF4V5pTnsbeygZRRs0EmYqFJKHQcUa62fStRzxMpRk/NZXdk0qsL5hIMctzMoTd2Dw67ICDNMGUz1INVhozmUEBo/Fxp39oy+CaLB2dXLTwUyqiYRPPWRad2cd3hoURWIVRjpFqB/558zexqpjvB/b7OKs6hq6qC1b4RHKMboqBqrErd2bzMd7mFohbdU9cz864sEgEZld4OTCXnpJNnjlyXzyixf1chXqxIiqpRSDuAfQKVS6oJEPEc8HD05n/aubjaOEiObcAjQy1NlhikHm5tDkZsWZlTNuNmAhvot/W5qaHf32vkHJlIBCC3QhAHx5Np9LK7Ioyw3dCihJar2NI6wWb1uM93YSS+eHvEwK1l9SyzjatL9laoxJqqs783j1by/uynxTxgq+NMiS+b/RWNXvSvmJPW+BMbVHKyiCrga2Aj8BjhKKfV/wTcqpa5TSq1WSq2uq0t8n95KPpYWoDBsuJ1Bw5TdEf1UFmGHKo+bYy4PbOx3U317V6+MKuhp/0mlamjYXNPKlgNtYVt/AOV56Sg18pUqb+1mdulippXkRzxuVrG1AzCGE820PLNZYox5qrbUtHH05HySbIp3diZYEGptdv/lTAh9e1axEVU+X2LXMUbRWrN7AHEKFvkZyZTlpo2KENBEiaojgDu01jXAf4CTgm/UWt+htV6stV5cVFSUoCX0UJyTSkVBOu/uGnu7V/qyq97J9fetweXuHumlCOHwdpshyFb7z+khL8IwZYuw8//yp4A9BWp7iyp3t4/Wzu5+lSrLqN5vJ6EwIJ5cuw+7TXH2YSVhj0l12CnJTh1xUdVds4ltvrKwO/8sSnJSyU5NYlMslSqbzewAHEOeKne3jx117SysyGN+eQ4rdyb4tb+z2QT+9jWpW2SXgs8DHWP/PSgR1LV34XJ7B2RSt5hbmn1QV6q2A1P8ny8GdifoeWLm6Mn5rKpsxDfGfVX/e28Pz6zbPzzlbGFghBimHFOlKj1Mpcpmh6KZ/URVo7N/RhVAmsNOst0mRvUhQGvNkx/u49hphf3Ea19MrMIItv+6u0huqWSbji6qlFLMLsmOrVIFY25Uzc76drp9mlnFWSyZUsCHe5txdiXwRNTa+Re2/ecPAJVYhZDEO0g5FPPKcthV76Q9kb/nGEiUqLoLOEkp9SbwJeB3CXqemDl6cgHNLg9bDoztvKpXNptoiKEMOvv3O5V85X8fjPx28IMFy1AeFKkQaeefRdj2Hxizep8dgIE09T6PrZQiN90xKnbCjHXW7GmmqqmDC8IY1IOxYhVGjIbtKHzspDymM/7ZJdlsqWmL7URzjI2qsfxUM4uzOGZqAd0+zepEnoi2WMGf4dp/MqomEpX+jKpJA2z/gTGraw2bYj1RSBAJEVVa6zat9ce01sdrrY/RWlcn4nni4ajJxmPw7nBtr00AuxucbK9tBxhS0/0ja6p5Yu0+nvxQzqKGBLe/WpGcgdaaZlfkYcoWEUXVuNlm91BQAGNPmnr/CkquzP8bEp76cB8pSTZOnzs+6rETC9Kpb3eP3Jly3WYAOnOnkZwU/aV9VnEWTreXqqaO6I89xkbVbNrfRpJNMaUwk0UVeTjsipWJfO1v2Wsuw+7+s0SV7AAMRWWDE7tNUZYXeiNILMyzzOojHAJ60Id/WkzIT6csN433KsduT/uVTaZKNas4a8i2jnq8voCy/8WzmxJbIj9U8PSIqraubrp9Oqb2X26aA5fbi7s7hJl13FxzGVStClepMo8lo2oGS7fXx9Mf7ePkWeNCDibuS0W+OcsesRZg3Ra82EgrnhXT4bP842o2xZJXlVE0pozqW2pamTYuk+QkG+nJSRxenss7OxIpqqrA5jA5VaGwUtWlUhWSygYX5XlpOAYQp2AxLjuVoqyUETerHzKiCoyv6r1djWO2zfXq5lqmj8vkjLnF7Kx3DolZfXttO+5uH59eOokDrV387bXtQ7DSQxx3j6eqyRk9Td0iJ1IS+rjZ5jLIVxWtUiWJ6oPjnZ0N1Le7I+76CyYQqzBCZvXuA5vY7RvHlJKCmI6fMT4TpWJsl6QXmvmT3WNDqG+paeuVKH/M1ALWVbckrorYWm2qVLYwb6lJyeZn2CbdgFDsbnAOqvVncf2JUzl1dhhhO0wcWqJqSj717W521LWP9FLipq3Tw7u7Gjh59jjmllq948H7w6xS6VXHVHDxwjLuXL4r0N8WBkig/ZdJk78FF+vuPwgjqrJLITWnl6iqb3fjsCuyU5P6HS7tv8HzxNp9ZKUkceLM2F6kAwGgI+Sr6q7ZxPYYTOoW6clJTCrIiG2wciBVffTbJ1o6POxr6ewlqpZMKcDr06xKVKeipQqyw5jULSSrKiRaa3bXu+IepByKTx87mbMi7NIdDg4pUXVUIK9q7LUAl2+rx+PVnDp7PHPLTO944xC0ANdXt5CRbGdyQQbfOXMWDrviZ8/0z0MS4iAgquKsVEUSVUqZvKrg9l97FwUZKSFDRXPTpf03GDo9Xl5YX8Ppc4sjJpMHk5XqID8jeWTaf14PyS272KbLws78C8Ws4qzYxtWMoVE1W/2bkWYF/RwWTswj2W5jZaJagJGCPy2yZVRNKBqdbtq6ugecUTXaOKRE1aSCdMZlpfBuojNLEsArm2rJTXdwxIRcSnNSyU13DMkOwHXVLcwtzcFmU4zLTuXLp0zn5U21vLZFBlAPmCCjeizDlC16RFUYMTRujgkA9bevG5zufnEKFrnpDrq6fXR6hmE8x0HI61vqaOvqjrn1ZzExP31ksqoad2LT3VSqCUzMj/2Mf3ZJNrsbXdG9lGNoqPLmwM6/7MB1acl2FkzITcwcQJ/XRCVEE1VZxdAqoqovlf6TkEmFg69UjQYOKVGllOLoKQW8u6thTPmqvD7Na1tqOXFGEUl2G0op5pRks3GQW0e9Ps3G/a3M81e+AK45djJTCjP46VMbQxumheh4/G+qjoxAllSskQoQplIFxlfV1RLIumlo7wqbnZSbJqnqg+HJD6spzExm6dTY/EkWkwrCiCpXI7z1J+hMkInWv/OvK286dlvkcUjBzCrOQuue6k5YAqNqRn/7b/P+VrJSkyjtM5h3ydQC1le3xDbvMAxen+ajqubeV7bVgPaG3/lnkVViRKlX/ieDqay3MqqkUjUmOWpyPgdau0Y2TyZO1u5tptHp5pTZPdu655Zms7mmDY934MJnR107nR5fYBglQHKSjR+cN4ed9U7ueXvXoNZ9yOL2e/aSM2h2ebDbQvue+pLrr2aFzZeyxtX4fVX17e6QO//MYxmBJi3A+NFa8/qWOk6fWxz3cNeJBRnsa+mgqzuoQuhqhH+fDy/9EJ7/7hCv1k/dFnwo0kpmx3W32f4dgJujJatb7b8xUKnaUtPGrOKsfm3xY6YU4NOwahD2j7vf2sX5f32rty83EPwZJqPKIqsE0NAuXYBgdjc4sSmYkCeVqjHJkkBe1dhpAb6y6QB2m+L4GT0jfeaW5gRGMQwUy6R+WFClCuCkmeM4ZdY4/vTyNmpbOwf8+IcsbhegwJFGo8tNXroj6jBlICC8wo6X6bMDsNEZXVRJpSp+atvMyIzZcXiTLCry09GanuwnZwP863yo2wozz4a1/4FtLw/xisG9fyNVvkIml8Y39qssN43MlKToyeqpuaDso95TpbVmy4G2kL6yIybmkpxkG3Beldaa+9/dA9C7WtXqF1XhRtRYSABoSCobXJTlpcWUrTYWODi+iziYNi6T/IxkVo6h4cqvbq7lyEl5gfYQmEoVwIZBZHKsq24hzWFnSlFmv9t+cO4cPF7Nr5/fMuDHP2RxO02aulI0u9yBClQ0kuw2slKSwrf/0vPNC/OBjbjc3XR4vFHbf2H9WUJYrN2vA2lHWL6QPQ0uI6j+fT40bIMr/wuX3g2FM+Gprwx5G7D7wCa26XJmxrjzz8JmU8wszoq+k9hmM9WqUV6p2tfSSVtndy8/lUWqw87CiQP3Va3c2chO/99GryykaCNqLKxRNRKr0IvKIYpTGC0ccqJKKcVRk/LHTKWqqsnF5po2Tp3dO9F5SlEmqQ7boMzq66tbmFOaHdKDMakwg88eN5lH1lSxZk8CxztobYyeBxMeZ8/cP2dsaeoW2ZFS1cG/A3BjUEZVtPafVKriZTBzyCb6A0D3798L/zoPGrYbQTXtFHCkwoW3mjfVl344dAv2dpPSspNtuozp4/ufIEVjVnEWm2pao/tM0wtHvadqi38n46wwVcYlUwrYsK91QCOc7n9vD9mpScwuye6d2t1SDSk5kNpfyPVCKlX90Fqzq945qJl/o41DTlSByauqbu6gqmn0+6pe9c/6O3lW76wcu00xqzh7wMnqPp9mw77Wfq2/YG44aRrjs1O4+ckNiRtE/fKP4C+LeublHQxYlSpiH6ZskZMWZWbfuNlQt4X6VnPGXBhGVFnPKe2/+Nnd6CTJpijLjX9kRmFmMuXJ7Zzy7rXQuBM+/gBMPbnngPLFcMz18P7dsPONoVlw827sPjd7bRMGtObZJdm0dXazryVKqz+jYNRXqixvWLisrmOmFKA1cU/WaGjv4oX1NVy8sJzFFXls3Nfa85rYUhXdpA5mB6WyS6xCEM0uD22d3VKpGusc7c+rem8M5FW9sqmWKYUZIVt0c0rNDsCB7GQ0iexe00bcuwo+eqjfMRkpSXz3rNl8VNXCw+9XDWj9EelohvfuhKZd8M5fh/7xRwq3a8CiKmoS+vi54O2io8Yk3xdkhG7/pTpsJCfZaJb2X9xYHo94TeoAylnHfUk/I7er2giqKSf2P+ik70H+VHjy/6BrCIKI/Tv/PAUzYvLu9WV2iREgUX1VGUWj3lO1eX8bpTmpvawSwSyYmEtKki3ukTWPrqnG7fVx5VETmVeWTVtXd89mp5a90Vt/YFqoEqvQi0Ccgoiqsc3M4iyyU5NGfQvQ2dXNOzsa+lWpLOaWmjPMmAai9sGqcB1epOF/V8Kj18Kmp/sdd8GCUhZV5PHr5zcP/diTD+41rbKSBbDijwfPi427PTBMucnpiSlOwSLiUGUImNV17QYgfPtPKUVumoNmp1Sq4mVPg2tg27vbDsA951Ksa/lu6g9gygmhj3OkwQV/g+Y98MpPBrdYCIiq1JI5A7q7VdWJugNwTLT/2gIzDUORkmRnUUVeXL4qrTX/fW8PiyrymFmcxVxrcK/VJWitjk1UgRFVUqkKYLXaD5aMKjhERZXdpjhqcv6oH668Yns9bq+Pk8PMMrL+uQfSAlxX1UJKko1p624xoycKZ8DjX4LG3jEKSil+fP5cGl1u/vzKtrifJyzebnj3DqhYBh+7G3zd8OrPhuaxRzqDzOMCR7oZjuz1xeWpyklzhN/9B1A0C1A4GswGgnCVKjAtQKlUxYfW2m+cjfNFvq0G/nUutFTx0Mw/8HTrNLyRWuYVx8BR18F7t8Putwe15s79G6nWBVSUjo9+cAiyUh1MyE+LPgMwo8jkpHV3Deh5Eo21GzpaovwxUwrYXNNKc4xxI5ZB/eNHTQSMCHXYlTGru13m9TPazj8LGVXTi131TpSC8oMkTgEOUVEFpgW4q945qiMDXt1US1ZqEkdOyg95+6ziLOw2NSCz+rrqFs4tPIBt9T/Ni/snHgIFPPQp8PT+mcwry+GKIyfyr7cr2RYtJDBWtjwDLXtgyRchfwoc/XlYex/sWzu4x+1qg78fC6/8dEiWOSD8nqp4gj8tcqK1/xxpkD+FzJZtpCfbSUsOP0IlR+b/xU2T3+MRTyo5rfvhnnOMYfmTD2ObfBxur4+aaK8tp/4IcivgiRsG5SnsrtnEdl9Z3Dv/gplVnB2DqPIHoY7S+X8769vp9umwJnWLJVONr2pljJ0Ky6B+znxjNE9OsjGzOMuczLZWm4OiZVRZZMmommB2NzgpzUmLeRTUWODQFVVTjFBZOUp9VT6f5pXNtZwwowhHGG9HqsPO1KKMuEWVz6fZvK+Zr7lvg8xxcNJNkDcJLrwN9n8IL/QPKPzm6TNIT7bz46c2Dk0a/cq/m+eceZb5+rhvmsiAF78/uErTs9+C2g2w4bHBr3Gg+EWVJWjiNaq7o42XGT+HQtf2sK0/i9w0EVXxsjtej0frflOhaquBTz4CFUt7BitHmwGYnAHn/wUad8BrPx/Ygn0+Upt3sE2XMaM4/p1/FrOLs9hV74z8dzfKR9VsCYyniSyqDi/PJdURW15VsEE9+I1/XmkO66tb0LHGKVhkFUNnM3jit2wcjFQ2uA6qnX9wCIuqOSXZZKYk8d4ozataV91CfXsXp4Rp/VnMLc2Ju/23u9HFud0vUu7aBKf/HFL9OwBnnQ1Lvwyr/9nPuF6QmcI3Tp/Jiu31/GP5zrierx/Va2DPO3D0F8Dmf6FKy4UTvwuVy2HLswN73HUPw4f3Q8E080bVkgBzfSxYlSp/eyE/I772H0TZtTduDoXuakqivBZJ+y9+4o5TePVnZmzQJx8xLT0IVLlimgE45QRYdA2svNVsGImXlj0k+TrZ56igKExmWSzMKsnGp2HbgQjG+cComhE2q3s6oer9fldvrmkjyaaYUhhZXCYn2VhckR+TqAo2qAcztyyHJpeH5hr/a2Esu/8gKFZBqlVgTjwmFcZwAlP1PvjGxti0Q1ZUJdltLKrIG7Vm9Vc2HcCm4MQZ0URVNgdau6hvj93nsGXnTm5MeoD20qVw2KW9bzzlhzDxGBNQWNc7+POTSyo457ASfvHsZh4ZzG7AlX+H5CxY8Ine1y+6xoQjvvh96I5TDDRVwtNfgwlL4JK7zHW73hz4GgeD31NleTZiDf+E4NDOyKLKho95yZG9GbnS/oub3Q0ulIIJsbT/tIZdb8D002DiksDVpblpOOwq9sHKp/0Eskrhiev7td6j4v8fdecPbOefhTWuZlNNhKp3oFI1wqLqjV/DnSeb4eJBbN7fytSizJiSuY+ZWsDmmjYaIrxu9jWoBzPPH77cUL0TUOb3FwvZklVl0eLy0OTyRPcv7n7b/L43Pj4s6xosh6yoAtMC3FbbHvEfa6R4ZXMtiyryovpx5vhfDDfG0QIsfe+XZNBJ8vl/gL4vxHYHXPpP49158GpTdbFusin+cPnhHDutgG898hGvbj4Q+zdk0bofNjwKC6/qH5ZnT4Izfm7yfVbdGftjej3wyLWAgkv+AcXzIb1gaEVVU2VsbUmfz4iq5EyaLE9VnO0/ILKJ1j8DcKaKLGxz0h10dfvocB9k4aoJZHeDk5Ls1Ng8Hs27zXb6Scf1utpuU0zIS2dPY5T2n0VqNpz/J6jfAm/+Jq71ar+oinfmX18m5qeT5rCzOVKyesBTNYKiytMB799jPl/zr143mZ1/sfnKlkyJHqvT16AezOwSE5rcWb/btPSSYvwfl0pVACtOIepO241PmMudryV4RUPDoS2q/HMAR1teVU1LJxv2tXLyrOi7eeZY42piFVW732F+/TM8kX4xycVhXoizS+GSO81Z8NNf6yUmUpLs3H7VYmaXZPGl+9bw/u44f3ar7jQJ6kddF/r2aaeasMQ3fmUG0cbC67+EqlVw3i2QO9HkwUw+3oQrDoX/q2Yd/OlwePf26Md6/NWJ5HQaXR6UImxmTiisJPRIlSpf3mS6tIPJvsqIjxUIAJUWYMzsbnQxMdbW367l5rKPqAKYWJAee6UKzN/9gk/Ciltg3wcx362jegMHdC4Ty2NsP4XBblPMKM5ic6RKVWou2JJG1lO17mHoaDQt/g//F6jstXR42NfSGdVPZTG/PIf0ZHvEaIW+BvVgUh12phVlolqrY9/5Bz2jag6W+JhBEFNGldaw+Rn/HVYMw6oGzyEtqg4rM4bFd+MUVW9vr+fz967G3Z2YHu8r/grQqVH8VGBaS2W5abH5qrwe9DNfZx+FrJsaRtRYTD0JTvwOfPRAz5mhn8yUJO655iiKs1P5zD2r2RrrjkBPh/FrzToH8ieHPkYp4/PqajNl/mjsehOW/wGO+CTMu7jn+snHm3EgDTtiW1skrH/q134evWxvVfaSM2h2uclJc4QcAxQOS4BFElWtbs12XUqJuzLiY+XG4s8SerE7njlklSuMz6hoZr+bKvKNqIprU8cZPzMttsevj7n97TmwiW2D3PlnMbs4i02RwoSV8mdVjVClSmsTQTFuDpz9O2P43vQUQOA1KNrOPwuH3cbiSflhQ0DDGdSDmVuWTWZnTewmdTDCNClNKlXAzjrzWhlxp+3+taYaXHyY6WC0VA/P4gbBIS2qkpP8vqo4RNXOunY+/5/3eWHDAXbVx1jej5NXN9UyIT+NaeNi280ztzQ7tvbfu7ejajdys/sqZk4ojn788TfClJPguW/3izoozEzh3s8eTXKSjavveo/q5hh2s3z0oDnLXPLFyMeNnwMLP2WqWvURsrFcjfDo581Z61l92iaT/cGLu16Pvq5obH3BxD50d0af2ebx/004TKRCfhytPzCz/yCyqKpvd7NFT6DQuT3iY+UE5v9JpSoW2ru6qW93x1ap0tqIqknL+rfQMS2N9q7uQKxGTKTlwbl/NLtX77sEPrgvctim1qQ1b2ebLmfGAGb+9WVWcRZNLg+1bRHsEBkjKKr2vGOqxkd/3vx/51YEWoCbAzv/oszfC2KJ3/4Ryo/6yJoq3F4fHz+6f+vPYl5JNuN0Pa60/pWssCjlDwA9tD1VXp/m8bXVHDExN2IsDJueBmWD0/wROWOgWnVIiyqAoyaZILhYBmy2dHi49t+rA9uOEzE7sMPtZcX2ek6ZNT5m4+nc0hx2NThxdnWHP6h1H7z+Sw4Un8CLvsURZ/4FsNlNGzC9wORXdTT3unlCfjr//sxRON3dXHXXu5HfQLQ2BvXi+VBxbPTnPul75ozuxR+Ef7wnbjD+jkvvCoyFCZA/BbLLB++rajsA+9YYU/2xXzGVu8q3wh/fq1LlCbTzYiUrJQmbiiyqGtq72OKbQFrnAegIP+zaav8NZHjsoUhccQpNldBaZURVCAKxCo1xvkbMOhtOvRkadsITX4LfTYN7zjX/O027ex/buo9kr5MDyRVxbYYI+9SWWT1SXlVG4ch5qt69zVR6DrvMtPgXXm12CzfsYEtNK1mpSZTmpMb8cMf4fVV9dwEag/peFlfkhZ0hCLCg0EeaclPtC50jGBbJquKFDTXsbnBx3XFTIh+4+WnzfjH5BPO7rxyhzUdxcMiLqqOn5KM1rIqSru71ab783w/Y0+DiT1ccAcDeeF8wY+DtHfV0dfuiRikEM7c0G62jvBi+cBP4unms+Cs47LbYM20yCk3iefNeszupT2tgdkk2d33qSKqbOrjmnlXhhd3O16FuEyz5Usgz+35kFsHx34Ctz5n79mX1XSZA9NSboeTw/rcrZbar71o+uK242140lzPOgGVfh5yJ8Ow3jTk+FO4gT5XTTX4cwZ8ANpsiO8qomkanmy3a33Ko3RT2uNxApUpEVSzEFadQ6fdTTT4+5M0xZ1WFYtnX4Gvr4brX4bhvmLDN578Df5oPty2D139lKjZ15nffXTAj/ucIwWx/lSfiuJqRav+1VJmqxcKrIdn/+1nwCTOgeM2/2VLTxszxWXHtgDysLIeMZHu/FuDKnY3sqnf2i1Hoy6x0Y7nY2pkb17dyqI+q0Vpz+5s7qShI5/S5ETom9dvNCKZZ5xoRPWmZVKrGAgsm5JJst/FulLyqXz+/mTe21vGTC+Zx1rxi0hx29g5g5l40XtlcS0aynaMmx372Y5nVN4YTVdtfMWGYx32DtxoymTE+i5SkOBJsJy6B035szhpW3trv5qMm5/PXjy9kXVUzX7xvTWiv2cpbIWNcb99TNI7+ojGev/A9Y263OLDRXDftVHNMOCYfb9qNB9bH/px92faCMaKOn2dezM/8JdRuhPf+Efp4tz/nJzmTZpd7QBWEnCihnfVON1t8/hf82o1hj7PiGcSoHhs9oiqGSlXlCuN/KgwtaMrz0lEqxqyqUCgFpUfAyd+HL70D/7cGTv8ZJGcaUXXbMvQDVwOQWjp3YM/Rh5x0B6U5qSzfVhfeV5VRFJeo0lrz5ta6yKGisbDqLkDDkdf2XJddAjPOQK+9n+01TTHv/LNIsts4cnL/vKpIBvVg0jtMC29tW5yt1+xS0/4b6XFaI8SqyiY+3NvMtcsmR/abbjZ+OWadYy4nLTMV4ua9CV/jYDjkRVWqw86CCbkRfVWPvF/FHW/u5OpjKvj40RNRSlGelzbk7T+tNa9uquX4GUVxiZ6SnFTy0h1sqA4hqrq74NkbIX8Keun/sb66JbbWX1+OuQFmnmM8RR892O/m0+aM51cXz+fNrXV886EP8QXPPavfZio+R14LSXEEFDpS4dQfG1H0wX/MdZ4OePgzkJINF/7dnMGEw6oi7Hoj9ucMprsLdrxmqlTWGfCsc2DaafDaL0L7Iqzdf450Gl3xV6rAGMyjtf/2k49Oye6X1RNMWrKdlCSbtP9iZHeDk8LMZDJTkiIfGMVPBeZ1pSQ7lT0DFVV9KZgKS/8PPvM8fHMrnP8XOsqO4QXvYiaUx2GUjsJnj5vCW9sbuOftytAHZBSAuy3mPK2736rk6n++x0ODybWzYhRmng15Fb1vW3g1ylnLUe5VcfmpLI6ZUsCOup5xZbEY1AP4w4Xfrou95QiYSpXHBV3xjxc7GLjjzR3kZyRz6aIoo302PQ0lCyDXf5y1y3aUV6sOeVEFpgW4vrqF9hCtqzV7mvjuo+s4ZkoBPzi3Zwr8hPx09jYObaVqw75Walo7OXlW7K0/MEOP55bmsGF/iB2Ab/3ZpIuf/Tuq2zVNLg9zByKqlIILbzXhmo9+Dl6+uV9b7bIjJ/CtM2fy5If7+OkzQeNs3r0N7Mmw+DPxP+/ci2DC0Sa5uqvNBIPWbYKL/m5G7EQiuxQKpg/cV7X7LVN5mn5Gz3VKwVm/Bm9XaL+X31PVZUuj0+OL21MFRG3/NbSbCpgaNydi+w9MC1CM6rFR2eCMbeZf0y4z8y2Mn8piYkF6YNv4kJI5DhZezYoj/8bnPV+P6PuJl88cO4lTZ4/jF89u4qOq5v4HWAGgMfiq3tvVyC+eNX+fH+4N8Vixsv4RU3E++vP9b5t2Gl1p47jC/mrMO/+CsfKqrHFlsRjUA7TspduWzIaW5EAmXUxYWVWHYKzC9tp2Xt5Uy1VLKiIb1Fv3QfVqmH1uz3Xj5kBafk/rfZQiogozXNmnYXUfX1VNSyefv/d9xuekcOsnFvaawZeIStWrm2tRCk6KU1SB8VVtrWnH4w0SOk2VsPx3MOdCmHYK66uN6BpQpQrMKJmrHoNFn4YVf4QHPmGEThBfPGEqn102mbvfquTB1XuNkXrt/cZcmlkU/3MqBWf8Epy1rPvdOWZH4DE3mNZfLEw5wSTyhvNARWLri5CU2t83UzDVmNbXPdjftO4XVc3dRkzFE/xpkRNNVDm7KMhIhnGzzU6xCG2E3LRkiVSIkT0NrthM6oF8qtB+KouK/Az2JMB3CfBRVTP/eqcSgOlDKKqUUvzuY4dTlJnCDfd/QGtnn7+dGEfV1LZ2cv39a5iQn87iirzAa0/caG1OysbNCZkHhj2JdePO4wTbR8xMi7/yM7c0m6yUJN7Z0RCzQT1ASzWejFIgzqH2h3AA6J3Ld5KSZOPqYyoiH2jF2Mw6r+c6mw0mHSuiaiywsCKXJJvq1QLs9Hi57t7VuLq6ufPqI/slm0/IS6e1szvyOJE4eWXTARZMyKUw0gyv9lp4/iZ45pvm49kb4dlvcXn9X/iuupvWx74Bz30Hnv8uPPRpY+Q84xcArK9uxW5TAzqjC5CUDOfeAmf91kQN3HW6EW9+lFJ87+zZTMxP542tdbDm36bUveQLA3/O8kVUTTiPwzwfskFP4q2KL8V+38nHm2pT9Zr4nlNrY5KffHyPMTaYcKZ1v6hq9AxcVOWmRxZV9e1uCjJTYPxc6GyJ+OKcm+6geQj/Rg9WOj1e9rd2xuGnGgeF0yMeVlGYTn27O2QFfCC4u308sbaai299i/P/+hZr9zTzzdNnRG9XxkluejJ/+fgRVDd38J1HPurtr4phVI3H6+P6+9fQ3tnNbZ9cxJIpBWyrbR+Yr2rPSmPKP+q6sK3WZ+ynYlOa7E3/i/vhk+w2jvL7qmI1qAdoqSIpz7Sm1sczf9UKAD3EYhVq2zp5dE01ly4qN69fkdj8tInK6ZsBN+l4aN7TfyfsKEJEFZCenMRh5Tm86zcsaq35ziMfsa66hVuuOCJkSm95XhowdLEKtW2dfFjVwinRqlSv/9KYvtc/AusfhnUPwUcPUFH9NBfbl5O1+SFTGfrgP9C4y7Sq/MM+11W3MH1cZmwjOCKhFBx9nRki21oNd5zUq89tsylmjM9i54EWePcOI0yKDxvUUz47/gs87j2W32V9l2v+8xGvbamN7Y6TjgNU/L6q+m1GLM44I/TtvUzrd/Rc7/dUNbrNG13eANp/VqUqnFm4ob2Lwkx/pQoi+qrM/D9p/0WjqsmF1jHs/IvBT2VRkW8E2mB9VbVtndzy8laO/fWrfOV/a2l0uvnReXN456ZTuOHkyMJuoCyqyOfGM2by7Loa/rMy6A0sw1+pitD+++Wzm1lV2cSvLjmMmcVZzCvLwevT4TfSRMKKUZh/WdhD3mnMZH3qQvOa54tfuC2ZUsCueie3vLw1JoN6gJYqHPkTKc9Li68SFxBV++Je61jm32/vxuPzcW20GIWOJvM/Nuvc/v9jVst9FFerRFT5OXpyAR9VtdDh9nL7mzt5fO0+vnn6TE6bE3pUjDVwdah8Va9tNiIh4mia1n3mhWPRp+Hbu+DblebjO7vh27tZ4rubXx7+Anx3D3x3r7l+4VWAEYoDNqmHY+pJ8LnXTI7Vvy+A1Xf33DQugxmNb5gsnyVxVJbC8FFrOr/P+iZ/+OLFTB+Xyef//T4vbYxh9mB6vhF08fqqtr1gLqeHEVUQZFr/Zc9Zp7sdktJo7DAv7gMxquekOfD6dNgKR4MV1eCfARhtB6C0/6JTWR9jnELjTvNmGMVPFfxYA4pVAD7Y08RX//cBx/7qVW55eRtzSrK5+5ojefUbJ3LNsZPJTo1fsMfDdcdN4cSZRfz06U09osESVWFG1Tz54T7++dYurjl2EhcsMCdz88vNa866qjhbgC3VJjF94VX9c+j8uLt9bK9tZ/uES03y9o7458MdM9X4qt7d1RibQR1MdbrdpKnPK82Jr/2XnAEpOYdUpcrZ1c29K3dz+pzxTC6MUg3e+gL4umH2ef1vGzfbvN+MYrO6iCo/R0/Jp9un+f2LW/j185s5d34JXzpxatjjJ+SZF8yhqlS9ubWekpxUZkfaFvz2X8yZ2LKv9rvJblPMKskK+89d09pJg9PNvKEUVWD8Rde+bMLZnv4qPPst8HYzrSiTT9mewZMzKbIwiZEddU6mFWWSl5HM/dcuYXZJFl/8z/s8ty4GX8KUE2Dvuz0ZUrGw9QUYN7dn50koQpnW3S5ITg9UhwYSqWBFIYRqAXZ7fTS7PBRkpBjBmFkcOasqw7T/4hqXcghihXRGbf9FyacKZuIAA0Bf21zLBX9dwUW3vs3Lm2r5xNEVvPqNE/jXZ47ipJnjsMUx9mgw2GyKP1y2gLwMBzfcv4a2To/ZdWtzhGz/bT3Qxrcf/ojFFXncdHbPXNGSnFQKMpJZF6+varUVo/C5sIfsrG+n26exzzrbvNmuuSe+58Bk7WWnmspyTAZ1MC137YPsMuaVZbOr3ml+PrGSfWgFgD64ei8tHR6uOz78e2qATU8Z31npwv63KWVOaHYtH7WRFCKq/CyuyMOm4M4Vu5hbms1vLz08YpBcdloSWSlJVA1RVtXWA23MK8sJ/5ztdaYSdPgVkDcp5CFzS7PZtK+1d5yBH+sscchFFRgD+8cfhCXXm9lc913CEe7VLLJtY+eUT0aOPYgBr0+zs66dqUUmDyYn3cG91x7N/PIcbvjvBzz5YZQy+uQTwOs2wioWOpqNuT1c6y+YXqb1FcZTlZxBo9O8wA509x+EFlWNfrFWmOkXa5ZZPQy5acm4u310DDYn6CBnd4OTrNSk6O3ayhWQOd74PaKQneogPyM5rqyqqiYXn7/3fVo6PPz4/LmsvOkUbj5/LlOKBj+GZiDkZyTz5yuOYE+ji5seW4+GkKNqWjs9fOHe98lMTeq3qUcpxbyynPhaZJ5OE6Mw46z+MQpBbPEHlU4vK4DDr4QtzxnfaRzYbYoz5xVz4syi2HdS+uMUyCkP7KaOaVSYxSE0qqbb6+OuFbtYXJHHooq8yAe7XSZXcdY54d83Jh1nOiBBXt7RhIgqP1mpDuaXG5P4HVctjrzdE/NCUZ6fPiSp6l6fZnejiymRyqLv/NXMnlv29bCHzC3Noa2rm70hqmfr97ViUzCnJP4sl5iwJ8GZv4Dz/wqVbzH1xWto1Wm8lTn4KtW+5g66un1MDZqFmJ3q4N+fPZpFFXl89X8f8OiaCDk4E48BW1Lsvqodr4L2xiaqoMe0/sw3jXHckUGTy01WalKvN5dYsYRYqHyphnYjqgJGz/FzoW5LWC+J9VjSAoxMpX/nX8RE7jj8VBYT89Pjav/96eVtoOD+zy3hU0snDbkJfSAcPaWAr582g6c+3Mf/Vu3tN6pGa803H/yQ3Y0u/vbxhYzL7p/bdFhZTnxm9fWPmCT5UDEKQWyuaSPJpphSmGnmhfq6jac0Tn5z6eHc/ekjY7+DNdjX3/4D8xobM1klh0ykwnPra6hq6uBzx0fxUoF57e3uMH6qcATyqkanr0pEVRC3fmIhT95wLKW5aTEdb2IVBl+p2tfcgbvbx6RwosrVaKIE5l0MheHPkOf6k9VDtQDXV7cwbVxmVLE4aBZeBZ96CpVVzH/t57OpcfAl2u11JqW874DpzJQk7rnmSJZMKeAbD33Ig6vCJO2mZELZ4th9VVtfMMNty2N8kU1Oh7N+ZfKztr0IyUZUDcRPBcZTBaErVQFRlRFUqeruNJsSQpAnoiom9jQ4ow9SbthhWjYx+KksJhWkx1yp2nagjUfWVHH1koqYX4OGiy+dOI3jphdy85MbaE/K7eWpuu2Nnby48QA3nT077CSIuMzqVoxC0eyobdYtNW1MLcokOckGRTPMCdSafw+oNRTPiBta/K812WUUZaUwPjuFDXGZ1UuMJyuOEVo+n2blzga21LQN2Y7SRKO15o43dzKlMIPTZkfwC1tsftpsTIj0P1Y00+xCHaW+KhFVQZTmpsX1YjYhL529Ta5B+1WsgMCwBr53bzcG6OO+EfFxZozPwm5TbAixvXdddUtiWn+hqDgGvraR18Z/mh1+QTQYdtSax5gaogWSnpzEPz99JMdNL+Jbj3zUe6dSMFNOgH0f9BsK3Q+f1wij6aebgdKxMvNsY1rXXkhOp8nlGfCQ24iiytkFBFWqopjVc6xRNbIDMCzdXh9VTR1MiiaqrDPjKPlUwUwsyGB/Swdd3dErNL9/cSvpyUl86aTorcXhxvJXZac5eKfGhs/f/ntrez2/fcF4UD9z7KSw94/LrL73Xaj5yOwwjiJ0ttS09R5Ps/BTJux4d4Sh50NBS5U58Uoxr0nzSnPijFUoMVU1V+TxaMHc9uYOrrhjJWfc8ibzfvQCC37yIuf8eTnX/Xs1P35qA3cu38nz62tYX90yav7fV+5sZF11C9ceNyW6F9DrMe3bGWeCPUIbfpT7qkRUDYLyvDRcbu+gB9buqo8gqjpb4d2/m3Lo+MgzvlIddqaPy+zX269t7aSurStQph4WbDamjstiR51z0KJzR107+RnJYSs/qQ47d1y1iFNmjeP7j6/n7rdCVG0mH2+Mpbvfjvxk1e+b9Obpp8e3SMu0bk+GlGyaXW7yB+CngqCWXQhRVd/ex1NVNAtQYUVVpMeKSneXSc6vGcTsxDHAvuZOun06EIEQlsoVZmNAQQxmWz8V+en4NFEr2mv3NvP8hho+d9yUAVc4E01RVgp/umIBezvTcbfWsq+5g//77wdMLcrk15fMj1jpicus/u5tkJoD8y+PeFhLh4fq5o7ekTdzLjA7697/V6zf1sBorYbsnvFAc8ty2F7bToc7xvZmIFYhthbg7gYnf3p5GyfPGsefrzyCb585i3PnlzAuK4XKBicPrNrLz57ZxBf+8z7n/mUFC37yEn95ZVu839WQc8ebOyjISObihWXRD979FnQ2905RD8ek48wu3Madg17jUDPyDfsxTE+sgmtQL4S76p2kJ9sZlxUiEG3VncanE6VKZTGnJJsV23ubSK0XssPKh1FUYdp1LR0e6tvdFIX63mJke207U4siv+GlOuz8/ZOL+L//ruHHT20kyW7jqiVBBtfyIyEpzfiqZp0d/oG2Pm8CU6edEv9CC6bClf+DjEIa/93AtAGai9Mcdhx2Fab910WSTfVsp09Oh/zJYUWVFT46oPbfszfCmn/BukfgC2+aM/ODEKtSHDFOQWtTqZp0XMx+quDH3NPgCllptfjtC5spyEjms8dNjvmxR4KlUwvRUyeTuvs5rvzba7i77dx21SIyoni/Yjart1TDxidhyRfDxihYbD1gTOq9woyT02H+x2DNvXD2bxL3N9tSBTk9O4PnlWbj07CpppWFE2N4zuBU9ZL5EQ/VWvO9x9bjsNv4xUWHUZzT37OmtabZZURmVVMHD6zaw19e3c4FC8qit7UTxNYDbby2pY6vnzYjtpiKTU+b1+ipMbz2Bvuq4jjJGQ6kUhUvXg9sfhZW3MKEXPPGNlhf1a56Z2iTrNtpDOrTToWyENtLQzCnNJvati7q2roC162vbkUl0qQeButNZLAtwB11zohvSBbJSTb++vGFJlvnqY2BF13ADHKeuCS6r2rrC8aXMdAX42mnQMnhNA+i/aeUIifNEVIINbS7yctI7l1KHzcnbACoVamKe/7f6ruNoJp7sTkjfPL/RmWpfSiwjORhPY0ADduh/UBcfiroiWiIZFZfsa2et7Y3cP1J00aFMT0axxw2CwBPWx2/+9jh0f83/X83MZnVV//TVJSPCh+jYLHZv/Ov3yDlhZ8yMSchBr8PGS1VkNNTqbKsFTH7qrJjH1Xz2AfVrNhez7fPnBlSUIF5zcjLSGZeWQ5nzivmV5fMx25T/Or5yLNBE8k/3txJqqPPyW04tDajaaadEnqCRV8Kp5tduKPQV3XwiypPhxml8urPYO+qASXuojXs/9CMf/n9LPjflfDyj5i86XaAkLvt4qGy3snkUJWY9+8xPffjvxXzY831t/iCfVXrqluYUpgR9WxyqLGM5dtrBy6qGp1uGp3ufib1cDjsNn576eFkpibx9QfX9p6FOOUEU9EJt+W6pQoOrIcZcbb++uDu9tHe1U1+xsDDGXPSHLSG9FS5e0zqFuPmGB+Jp7Pf8akOOylJtvjGKe1dZapUU0+BS+6EU2822TGr7ozzuxgb7G5wkeqwha4UW8SRTxVMYWYy6cn2sFlVWmt+88JmynLT+MSSGDOSRhibf4bnPy+bxJnziiMfvO1l+M0U+MNcrq78Fl+3/Zf9b91ndqx6+5itPZ3w/t0w86ywsTHBbKlpJSs1idK+QqNkPpQeYVqAiTgR6GozbaqcnpZWSU4q+RnJrK+OcQdgpt+0HSVWodHp5mfPbOKIibl84ugYxImf8dmpfOGEqTy7rob3gsavDRcHWjt5fG01ly2e0G/EW0j2rTEnb5F2/QWjlKlWjUJf1cEvqtoPAAqW/x7uOhV+Nx0evQ7WPWx21UWi7YAJ3Pz7sXD78SaMbtKxcOUDMO9SUlb8luPSKgcVAOrx+tjb1MHkvqGDnk5468/mD2fi0TE/3pwQOwCHPEk9RoqzU0lPtg+qUmXdN5ZKlUVRVgq/uGge66tb+cur23tusN4Qw1WrtvpT1GecOZClBhhM8KdFuKHKDc6u/rMhx802Z/f1W0I+Vl56cuzG1bYD8OBV5g3jkjuNWX/J9SbA9YWbYP9H8X4ro57KBhcV+VHiFCpXmJZNfgzbwoNQSvljFUK/Rjy/voaPqlr46qnTSUlK8M7cocKfqj4rK8rf1PpH4L+XQ3YpVCwlz3OA6+zPMPn1/4O/HQW/KDWvq49fD+/cCm/+NqYYBYstNW3MHJ8V+ve28GqT3xbvzM9YCMQp9LT/lFLMLc2O3axud5gdbK2RM/Z+/swmWjs8/PLiw+IOfb3u+CkUZ6fy06c3hswuTCR3v1WJ16e5dlmM/y+bnja2i1hjbMBUjdtrzK7cUcTBL6ryJsFnX4Abd8Ald5kdWttfhkc+C7+dCnedAW/+zgzt1NqImQ2PwX2XwR9mw4vfB0canPN7+MYWuOzfMPNM83V2Kb9Wf6W2fuBnAnsbXXh9ur9Jfe1/zB/M8TfG9Xg5aQ4m5KcFzOp1bV3UtHYO386/IGw2xZSiDHbUDWxMB/Ts/Iu1UmVx5rwSLjqijL+9tp0P9zabK0sWGBNruLyqrS9AbgUUzhjweqEnoHMwPrucNAfNHf3ftBra3RRkhqhUQdhk9dx0R2ybKbrd8NCnjIfv8vtMYjuYEL4L/w7phWZId1dbxIcZa+xpjBKnoLU5I44jnyqYSQUZIdt/3V4fv3txC9PHZXLxwvIQ9xylRBlVA5g23sOfhfKj4Jpn4ZJ/kHT9Oxxn/w+/n/pPuOh20+JLyze7bV/4Liz/ndl4MfmEqEvQWrO5786/YOZdCo70ASWsR6W1J/iz11OW5bD1QFtMOz0BI9IjVKre3l7PI2uquO74Kczq2+KMgbRkO986cybrqlt47IPquO8/UNq7urnv3d2cNa8kdj/X5qfN/1d66EiOkFgnyZVxjiBLMAe/qLJIz4fDLoWLb4dvboNrXzGCpbsTXv0p3LYM/jAHfj/DvHHUrINjvwzXr4LPvQJHXtv7F56WCxfdTrFvPxcc+POAl2Xt/Ovl5/B6YMUt5gUpznYDwNySnEAejHXmNBKiCmBaUWZAGA2EHXXtpCTZBpTbc/P5cynKTOHrD641Pg6b3b8VN8Q/odtlxNaMMwf0xhlM0yDS1C1y05PDGtULMvpUqgqmml2HB0Inq+ekOUIGifbjxe/Bnnfg/L9A8bzet2UUwKV3QdMuePrro67kPlB8Ps3uBlfkOIX6beCsjdtPZVFRkM7exg68faoFj66pZkedk2+cPhP7MI2eGRLSLVEVYqiy1rD8D/D018wO2k8+YnbyYao5M8sLeamhyEyGOOPncPXjcOM285p81eNmMkMM/3/7Wjpp6+zu76eySM02fsB1jwz9SYCVpp7de0fbvNIcPF7NtgMxvt5lhR9V0+nxctNj66goSOfLpwx8aPaFC8qYX57Db1/Ygss9PNlW/3tvD22d3bGFfQLUbYX6rbG3/izyp5if4SjzVR06oioYmx3KF8NJN8Hn34BvbIULbjVtthlnwVWPwdfWGy9JUYSqxaRjWVlyFed4XkZvfHJAS7FEVa809Y8eMOFyx984oDf4OaVmFlV7V3fAOGkFgw43U4syqW7uGPA/9PbadiYXZgzoTScnzcFvLp3Pjjonv33B3xqbcoIZb9DUJ8+qcrkR2PGUn8PQNESVqr5CqMPtxen29q9U2R2muhamUpWXnhyy6tWLtffDe3fAMTeYk49QVCyFE28yI3nW3hfrtzKqqW3roqvbF3nmXyCf6rgBPcfEgnTcXh81rT2et06Pl1te3srhE3I5Y24MoYijiZQssKf0r1RpDS/9EF75MRz2Mbjivn6m47Bm9cxxZkB7hJE0wWypMSeNvXb+9WXRp8DjhJW3xfSYMdNSBcrWs4PPz7wy8xob8zieCKNq/vrqdiobXPziosNi2zkXTPNes7Hkwwew2RQ/OHcONa2d3PFm4uMHur0+7n6rkqMm57NgQm5sd9r8lLmcdU58TzZKfVWHpqjqS9Z4OOIT8LF7TCVr6skxBz/umvdl1vkmoZ/88oDGDuyqd5KT5ugx8/m8xv9VPB+mnxb340GPgNq0v5V11S1MLswgK8ET7cNhjZbZOcAW4I46Z9ytv2COn1HEJ5dM5J9v7WLlzobwvqqtz4MjY8DViGAsUZU3CE9VdpqD1s7uXtUNK/izsK+oAtMCjJBVFbH9t+8DeOqr5mdz6o8jL+y4r5v2zDPfhNrN0b6NweHpSPiLZUxxCpUrIKs0bj+VxaQQOwD/s3I3+1o6+fYZM+NL8h4NKOUfVRMUXOnzmjfyt/9sqvoX3REywPGw8jiS1SNg7fyLOKuv/EhTrXrt52ajxVDRUm3+Huy9N/5MzE8nKzUpdl9VVokRpt7e/5tbatq47Y0dXLywjGOnFca+ru4u897x1yNNqvxj18HzN3HkhGzOOayE29/YSU1L/80sQ8lLGw9Q3dzBZ5fFEQ2y6WkzPDmnLPqxfZm0zFSR67fGf98EIaJqkJQW5PBVz/XmDeDxL8Y1dgDMi3ovP9X6R02g2QCrVBC0A7C6hfXVrSPW+oMeL9RAzOqdHi97myLn+8TCTWfPZmJ+Ot986EPas6eZnTfBviqtYeuL5kw5aeB5WhZWFMKg2n/+VPXgyfc9I2pCrHHcbBNIGCIxPifdVL1ChrA66+GBq0yl4NK7+71R9MNmh4v/YZKkH/q0aZsmgn1r4Xcz4NlvJubx/QTiFMJVqgYw768vE/N7sqrA/E7/9tp2jpteyNJ43jRHE+kFPZWq7i54+Br44F7zunX278IOw7U2zMSUrB6BLTVtlOakBqYPhEQpuPBWKFsEj3zOnDwMBS17QwqAgFk91h2A2SWA9m+mMvh8mu8++hFZqUl8/5w5sa9p+ytw6zHwyk9g+qnw5Q/g6C/Ayr/BfZfy3ZOK8frMTtNEcvdblZTnpXFqLCNpwAjUfWtiC/wMxeTRNwdQRNUgmZCfxg5dxrp534Kdr8F7t8d1/111QaLK5/ObNWfH318OYnx2CgUZyazY3kB1cwfzRqj1B6YCYFMMyFe1q96J1vGb1PuSnpzE7z92ONXNHfzsmU2mIrPrzZ4qyIENxnwab4p6GBqdbjKS7YPazRVqVI1VqcoPVamy0vZDtADz0pNxe3109G25eLvNm2F7LVx+b48BORpZ4+HiO6BuMzz/ndjuEw/Ne+H+y0w7dtWd8OEDEQ9vcroHvAN3d4OLJJuiJEz+D/VbB+WnAjP+ymFXgViFO5fvosnl4cYzZg74MUecjCIjyLva4f7LYeMTcMYv4OTvRxSfcSWrR2BLTVvvJPVwONLgyv+a9d5/RY8fajD0yagKZl5pDpv2t9LtjeHkOhAA2tMCvO+9PazZ08z3z5kTm32gea85KfrPxYCGTzwCl//HVFXP+rV/wP0Kyh8+hxsXGR/fR1XN0R93AKyvbuG9ykY+dcyk2O0am58xl7POG9iT5k023rZdIqoOGsrzzFnoipzzjcn5pR+FNQz3pdPjZV9LZ4+o2vy0eaM6/pthz/RiQSnFnNJsXtti8phGIk7BIiXJTkVBRmAocjwMJE4hHIsn5XPd8VP436q9bEpdYM4O6/w+q63Pm8shElVNLveg4hQgaLyMq3+lqjBcpQpCtgCtqle/FuDLPzLi8rxbTK5PPEw9GZZ9zZ+4/jDPrdvPdx9dx7/eruS9XY20dkZoN0aioxnu+5jZhfu5V2HiUnj6qz2/qxD88MkNXHbbOwPaNr67wcWE/HSS7GH+3wL5VAPzUwHYbYoJeensbnDS0N7Fnct3cta8YuaX5w74MUecjEJTGb33QlP1veBWOOb6qHeLOVk9Au5uHzvq2pkVa5hx5jj4xIPgcRlhNRjjus9nYhDCiaqyHLq6fbHteO4zquZAaye/eW4zx04riD7WxWr1/e0o2PYSnPwD+NJKU6UKZuFV8OmnoauNazdfywXp6/jp0xsHPTosFPe8XUl6sp3LjpwQ/WCLzU8ZP2gk73IkLF9V5YpR46sSUTVIUh12CjNT2NvUYc4KUrNNqTlEEGNfKoOTnLU2OS35U2HuRYNe19zSnIAfZ+4IiiqAqUUZ7KiN31O1vbYdpWBKlBE1sfL102Ywc3wW3/7Av4vT8lVte9HELWSXhL1vPDQ53YOe3xa6UuVv/4WqVOVMgOQs8z3tWwuNu0wOm88bJNCCzOrrHjZp/UddBws+PrBFnvQ9mLAEnvoK9z33Ov9btYcfPbmBy25/h/k3v8iyX7/Ktf9azR9e3MJz6/ZTWe+MLHy63SYjq2G7qZwVH2Z2HDrS4cFPmQkDIXi/spF9LZ18NIA36t2Nzsh+ql3LzZlw3uDGx0wsMFlVf3ttBx0eL984fQxXqcBUftoPmFDky+41ntQYmV8eQ7J6BHbWt+Px6sgm9b6Mmw0fu9ucdDz82YGFQAO46k1ae3Y4URWHWT2r1Fz6vbg3P7kBt9fHzy88LLLPbvsr8PelptU39WS44T1zIh7OujBxCXzuNVT+FG7x/YpFe//F8+vi9/9Gor69iyfX7uOSheWRW7LBuBqh8q1BdWUAc8LjqjcFiVHA6J+JMAaYkJ9mRtVkFpkztvs/Zv7gz/xFxPtVBu/82/q8mcx+wd9iNslHwgoBnZifHvsfeYKYWpTJm1vr8fp0XLv4dtQ5Kc9Li3/3SxhSkuz8/rLDufBv7dRnlFC46w2YdwnsfQ9OiD21PhpNLs+g/FQQRlS1d5HqsJGeHOLnoRSULoCNj5uPIE51ZPJWSgq5DxZCdoHZ4r7zdTOO54zIf6MRsSfBpXfh+/syvt3+K1ad+QBnL5jEpv2tbNzfyqb9rWyuaePVzQewtFRGsp0FE3P5x9WLSU8OevnRGp76shGFF95mdmmCCY685B9w78XwzDdMXlbQG05dWxf7/ObblzbWxL7jCJN1tLvexaJws9osP9W0UwYds1GRn87KnQ1sO9DOpYvKB93SHnGKZkFKthG/U06M667zynrM6jHNyevD5v3WeJo4RBWYcV9n/8b8Hb3wPTjrV3E/Ny17zWWYStXkwkzSHHbW72vhkkVRssfSC8CWBG37eWnjAZ5bX8ONZ8wMPy6powme+oppteZPMa2+vpWpcOROgM+8gH78S3xn4/946Yl9dE3/LylpQ/N3+N939+D2+vjU0knRD9baBHau+gdo78D9VBZWa75yRU/FfgQRUTUElOel9/SpZ5xudr+s/Jv5g596ctj71e/dwrX2Z5j97C1QvQpyJ0adzB4r1g7AkWz9WUwdl4nb62NvoyvyfLU+7KhtH5LWXzDzynL4yinTeem1WVyy402St70A6CGJUrBocrkjVz9iIMeqLnX0bv8VZKSEP4u97N/mTLyzpddHa0Mtb3+wjePSksmwdRn/WPliE4YbYodWfAst5615P+a41V9mYtWvyVnyF4pnjeOkWeMCh3R6vGw90Mam/a2s3NnIYx9Us6qyiRNmFPU8zhu/hg//ayIbFlzZ+zmmngwnfBve+BVUHGtaGn7WVTcDpl360sYD3HjGrJiX3uh009bVHT5OoW6LOQMegh2hEwsy6PT4SE6y8ZVTBxcuOypYeJV5rUqKvyIbbFYfiKh6fn0NBRnJA3ttOPJaaNhpXp8LpsY0YzCAq9EMaoawO9XsNmO92BCLWd1mg8xiPC37+OGq9cwcn8V14bKdut3GO7VnpfGtLf1y/JtqktOxfexuKh+fzClr/0j9racx7tqHB7brLgiP18e9K3dz/Iyi8CcLnS3mhGn7K7DjFWjeY66ffLzZ+TcY8iZBzkTz+PH8PhOEiKohYEJeGs+v399TiTntp+YX/PiX4Itv94SGam1KlJuegk1P8smadeAAvPNNK2XBlYN/k/MzuSCDwyfkctqckc/ACR6sHKuo8vk0O+vbWTq1YMjX88UTp3LL2iNJbnuN7jd+T1LGOCiJ01MUgSane1BxCtBTqQqe/1fvdIeOU7BIzw8pANwtndy46hV+vmBeXPPDYuWB1sPYZLuc67Y9AH97D875nZnf5ifVYWd+eS7zy3M5+7ASHl9bzdo9zT2iau398PovYcEnwlcMT/iWCSZ99pvG/+UPJ/2oqgWl4Nplk/ndi1uprHfG/DdmGcfDCuBB5lMFY4WLXrWkgrIBBNmOSgYgqGBwZvWG9i5e3nSAa46dhCOcDy4ap//U7LB+7lumrRut2tNea1rlq+4Cd7tJax8/L+zh80qzefj9Knw+HXW0jDdzPNu3b6OmtZO/fnxh6O9JaxOmWrncRFUcPogTb6WYdNGP+PP+XD574Bf47jgR2xX3w4QjB/yQz67bT21bF7++ZFLPlT4f7P8Atr9qRNTe90xVKjnTCKmlXzYV4AHGlPRj0jLY9oJ53kH4kYcC8VQNAeV56Xi8mgNWuF9yutl27qw35drq9+Hlm+Gvi+HWJfDaL8CRwb+yPsf1hffAF5bDCTeGLSkPBJtN8cT1x3LhEYM7CxkKphXFP1i5urmDTo8vkHM1lCTZbVxyifERJTVtNwb1IfpH7Pb6aO3sHrSoSkmyk+qw9Wv/FfSd+xcDoUzvQ4XPp3l7RwObZ34JPvOi8RT+9wp44JM9M9KCyEp1MH1cJmv3Npkrdr5u8o0mnwDn3hK+zWazm1mEqblmlI7fbPxRVQvTijK5YIH5O39p44HQ9w/B7kBGVRgRVrnceGdiGO4bjWOnFfLlk6cNKh37YGEwZvXH1+6j26f52OI4zNB9sf6Wxs81sSDhNha1VMGz34JbDjMzYGecaU6SL70rokVjblkOTrc34JkNx7qqFlYccGB31vDds2axqCJM1W7FH83YshO+PThBFcTZl36WS7t/QpMnCe452wyfHiD3vF3J5MIMc5LU1Wbaq7+dCv84GV77mdkgcOxX4NPPwLd2md2YR31u6AQV+H1VDVAXOgB5OBFRNQRMyDdnnnuDJ9GXLoCTvwebnjR/XG/92ZiJz/kDfGMzfPYF/tJxJpnFU0dm0cNITrqDwsyUuLKqtg/hzr9QTJ40mYZ087PfPz7+UUDhsNp1eRmDrzjmpvUehGzaf/GLtVSHEWgxD1WOg437W2l0ulk2vdBMJPj8m2YSwbaXzc6klbf1MwUvmJDL2r3N6AMbTEujcIbx5kSrfGSOg0v/aaoMT30F7fPxUVUL88tzmZCfzuyS7DhFlQulev5/ezEE+VTBpDrsfP30mSPubxwtDMSsrrXmodV7OXxCbuTQz1hIyYQrHzCX911mBolbNO40Qv9PC2D1XaYydf0qI6as6JIIzPPnBK7fF7oFqLXmzuU7ufjvb1Gr85iS2sZ1x4d5H9jwmEmon3cpnPjdeL/LsEwbl8nRRy/j1LabcZYcY/yMT33F7CqMg7V7m/lgTzOfOqYC24F1cMeJZu7jtFNNYeGb2/2vCT8y/0sDrG5GJdhXNcKIqBoCrFiFqqaO3jcs/TKc8kNjrr1xu5lzdeRnIauYtk4P9e1dcXmMxjJT4xysPNBByvGQNvs0OnQyDzROG7LHbB6CNHWLnDRHoFKltabB2RU6oyoGjEAb+krV8m1m/tsyK8TS7jBRC196x+w6ev7bcOcpZkeinyMm5uFw1eK91z/09hMPBebDRWXSscZTsv4RWpbfTn17F/PLzX1PmzOe1bsbaWiP7Y1hd4OL0py00HlidZvNme8gohSE8ASb1WNlXXULm2vauGzxEFX0c8rgyv9BR6OprlavMTu3/7LIZKMt+pQJ0bzwb1AY+2vE9PGZJNttgRFhwTS0d/GZe1bxs2c2ccKMcZx77EKS3K2hQ3SrVsNjX4AJR5sNTEOcvP+VU6bjTcnlOt+30cd+Dd6/B+45J67JIPe8tYvMFDtX2F6CO081O3Q/9ZTZXDL/MrN5azjInQi5FaHnug4zQy6qlFJJSqk9SqnX/R+HDfVzjDZKc1NRCvb2DSC02eG4b5gt632mb1fWm2MnHyKiatq4TLbXtsecj7KjzkleumPQ0QSRSD/9+/y49O88+FHzgDKOQtHoH6Y81KKqrasbj1eHzqiKgaijagbIW9vrmTk+i3HZfcIz8yfDJx42Ke2t++AfJ8Hz34WuNo4odvDP5N+iO5pNflC8be9jvwbTTiP7jR8wT+0MiKrT54zHp+GVzbUxPUxlQ4Q4BeuMdwhM6kJ/BpKs/tDqKlKSbJx3eOnQLaR0gWkF7vvA/I1ufhqWfAm++hGc83vzZh0nDruNWSVZ/cbVvL29nrP+tJy3tjfw4/Pn8o+rF5FW4P/b7ztYuWm3EXqZ4+GK+8ERJpx2EORlJPPts2bx1s5mfu+7Ej72LziwEW4/Hna/E/X+ta2dvLFuBw/k307qCzeaE5AvrBi5/5lJx8Hut+KeajLUJKJSNR/4r9b6RP/HugQ8x6giJclOcXZq/0pVBHbWm0rMoSKqphZl0tLhCWQtRWNHbXvit52nZHHM0cewr6WTlbsaoh8fA9bcv8FGKoBpm1rVpcCImoFWqtIdtEQbqhwnnR4v71U2hp9PphTMuxiufw8WXQMr/w5/O5qZL13DLLWHByb9FEoOj/+JbTa4+A7ak/K5NfnPzM7z57GVZlOakxpzC3BPgyu8qNr1pmnX5w69sV+I36ze6fHyxNpqzppXTPZQzzGddY6ZEHDiTfDV9XDGz3uCOQfI3NIc1le3orWm2+vjty9s5hN3vUtmahKPXb+UTy2dZHbxhkhVp7PFpNR3u00VN9ZJBwPg40dN5PLFE/jra9t5qvso+NwrpiX6r3PhvX9EDNR86aXneCLpO8xpfsPMDP14YtcalcnHmdiJ2tjCtxNFIkTVEuBcpdR7Sqm7lFKHxA7D8ry03p6qKFTWGz/HYLfejxUsw3msZvUddUMfpxCK0+cUk5mSxGNr+puqB0KTXzQORYUtJ80R2P1ntbQGYlSHxLT/VlU24u72cdz0KC+kablw7h/gsy9BWh5q7zv8M/cGHmqJPf6gH+n5/DH3u5SqBlKf+T/QGqUUp80Zz/JtdXS4Q3h1ut1mJ1fNelwbX+DEjpe5oP0hU0F76Bq4+xz4y2L45QTjhRwiP5XQH6UUh5XHblZ/YUMNrZ3dXDYYg3ok5l8GJ34bMoZmt/G8smxaOjy8u6uRy+9Yyd9e28GlC8t5+v+WBWazAkGiyl+p8nYb83zDNrj831CU2JBYpRQ/uXAuiyvyuPHhD1nnLoXPvWZiTJ79JjxxQ/8ga63pfuuvXLbuWjKSQF3zHCz76ojvuhstvqpECJ5VwKla6/1KqX8DZwNPBh+glLoOuA5g4sT4y6ujkQl56by7qzHm43fVt1OaM3TBlqOd4MHKS6ZEfuFqcrppcLqHRVSlJds5c14xz62v4ScXzCMtVLBmHFgttqFo/+WmOQLG9/rAMOWBPW5ehoOm3UMrqlZsq8dhVxw9JT/6wWC2bV/3OjTupHG1ZuOKnXR6vAP6H9Ba82hdGYtKvsi5m/8KT1wP6fl8sa2WI6nEeddfSUvqNGf9Xa3msrvnzSEd+H0ysBOoyjIm+MzxJqoh8xTz9WGXxb0uIXYOK8th+bb6mP4GHn6/ivK8tKivHaMFy6z+8X+sJD05iT9dsSCwQ7UXwaNqtIbnboQdr8J5f447VHWgpCTZue2qRZz/lxVcd+9qnrjhWMZd+YDJhXvj16byc/l/TJve1QhPXE/Slmd5ybuIrEvvoGDi0HlSB0VOuYnI2LUclnxxxJaRCFH1kdbacoquBvrtIdZa3wHcAbB48eLRMbBnkJTnpfH42g48Xl9M+Sm7GlyHTOsPoCQ7lTSHPaZxNdYuweFKnb54YRkPv1/FixtrQr/wxUGTy02qwzZocQamUuVye/F4fYFhyoUDrFTlpCXT0uFG+ys6Q8HybfUsnJjXOxk9GnYHFM1kwYQaPN6Bp2rvaXTR0uGhbcF1UFQHa++DpDTGp2Yzx56EszWHwtJikySdkm2iHlJzTCRDRhFv1zr4zosHuP0LZzO7YnCtHmFgxJqsXtXkYsX2er5yyvSouU+jhZnFWeSmO5iYn85frjwifGxHao7ZrNFWAytvNTvnjv2KMckPI4WZKfzjU4u59O/v8IV73+e/1y0h5aSbTHv+0c/D7SfASd+FFbeg22q4I/1zPGw/lxfnjrLd6xfdbqYwjCCJEFX3KqV+DqwHLgQGMQdj7FCen45Pw/7mTiZGaelprdlV1875C0b2lz+c2GyKqeNiG6w8lIOUY2HJ5AJKc1J57IPqwYuqIQj+tLBS1Vs6PAFP1UDbinnpDjxejcvtJSNl8P/29e1dbNzfyjdPH1g6+BETcwH4YE/zgETVh36D82HluXD0HWZ3lN2BAv70vw9Ysa2e9z5xatixSGvrtrNHw8TiEfSAHOLEmqz+yPumNX9ptLEvo4hUh503bjyJzJSkyKO5lDLVqs1PG3P67PPglJuHbZ3BzC3N4XcfO5zr71/D9x5bz28vnY+adY4ZbP7AJ0z+VG4Fm85+iF8+7OLnF00eshO0IWPi0SO9goR4qn4C3AusBd7RWr+cgOcYdZTnmaybqr47AEPQ5PLQ2tnN5MIxPv8rTqYWZQaiEiKxvbadlCQbZXnDkzxtsykuOKKM5dvqqW2LPgg7Ek2uIRRVaT2hnQ3tXWSnJpGcNLB/Wcs43zREWVVvbfdHKUwf2Jbp8dmplOSksnZv84Duv66qmeQkW8/8t6BJBKfNGU+D082aPU1h77+73kVhZsqQCExhYMRiVvf5NA+9v5elUwsC0TVjhZw0R2yzTrNKoKnS7ES86I4R9SadM7+EL58ynYffr+Kfb1WaK4tmwLWvmHDez7/J37bkkJPm4KJRECw9Ghny357Wer3Wer7W+jCt9feG+vFHKxP8//D9YhVCsCuw829svUgMlqlFmVQ3d+Byd0c8bkedk8mFGXENXx4sFx9RhteneXLtvkE9TpPLMyTBn9B7qHK90z1gk7p5LCP0hsqs/tb2erJTkwY1W/KIibk9yepx8mFVC3NKskO22k+YUYTDrnhxQ02IexoqG5yB0THCyBCLWX3lrgaqmjoSZ1AfDRTNMtENV/7PTOMYYb56ynTOmDuenz+zkTe31pkrU7Nh8TXs60rh+Q01XHHkhPja/ocQEv45RJTkpGK3qZhiFXYFMqoOrUqV5ZHaGSUEdEdde0LG00Ri+vgsDivL4bEPBr4LcG+ji3XVLUPWtgye/9c4wDR1i7ygVuJg0VqzYls9S6cWDkr4LpiQy97GDupjDOu08Po0G6pbOLw8tKDLSnVwzNRCXtp4IGwu2p5GV3ifizBsHFYWOVn9odVVZKUmccbcg9j3dvZvTWL7IGMchgqbTfGHyxYwY3wWN9y/hp1Blo17V+5Ga81Vx0jUSDhEVA0RSXYbJTmpMcUq7Kpvx25TgZbhoULwYOVwdHq87G10BeYFDicXHVHGhn2tbD3QNqD7//r5zdiUGdg8FOT624jNHW4anF0DzqgKfqyhaP/trHeyr6XTjKYZBAsmGB/N2j3N8T1/XTtOt9f4qcJw+pzxVDa4QkZ4dHq87G/pPGTiTEYzkZLVWzs9PLtuP+cfXnpw75K22RMS7jkYMlKS+MfVi0my27j236tp7fTQ4fby3/f2cPqc4jHXih1ORFQNIRPy0mOqVFXWu5iYnz7wKetjlEmF6dgUEX1VlQ1OfJphr1QBnL+gFLtN8egAMqve393I0x/t5/PHT6UkZ2jEcqD95zJG9cG0/4ZyqPIK/2iaqPlUUTisLAe7TcXtq/rIb1IPV6kC46sCeDFEEKh14iOiauSJlKz+9If76er2Hdytv1HMhPx0bv3EQvY0uPjyfz/g0Q+qaHZ5uObYSSO9tFHNofWunmDK89Ji8lTtrD80/RwpSXYm5qdHnAFoVRZGolJVmJnCCTOKePyDarxxjK3x+TQ/eXoT47NT+PwJQzd5PTvVeBYaXR4aXW4KB9H+C/ZnDZbl2+qZkJ826PZZWrKdWcVZAxBVzaQn25kS4W9kfHYqh5fnhBRVlQ2WqJL230gTyaz+4Oq9zByfFRhDJAw/S6YU8OML5vL6ljp+/ORGZpdkc9TkGHPpDlFEVA0hE/LTOdDaRVd3+MnrWmsq652HnJ/KYmpRZsRU9R21TpQaufE9Fy8so6a1k5U7Yx9b8+SH+/hwbzM3njFrSM2bSXYbWSlJVNY70XrgaepgtninOeyBxPeB4vH6WLmzgWXThmZQ6oIJuXy4N77Zix9VtzDPX+WKxGlzxvPh3mYOtPbe0bm7wYj6Q/HEZrQRzqy+7UAba/c287HF5aNv2/4hxieOruDqYypwe31cc+wk+X1EQUTVEGJ5pKojtAAPtHbR4fEecjv/LKaNy2RXvTNsJWhHXTtluWlDEp45EE6dPZ6slKSYW4Adbi+/fn4zh5XlcHECthhnpzkCcyIH46kC0wJsHmSl6sO9zbR3dQ+69WexYEIubV3dEX12wXi8Pjbua2V+DLsOT/ebm1/e1LtatbvBRU6aI+AzE0aWUGb1h96vIsmmuFC27Y8KfnjuHO7/3NFcunDsZIWNFCKqhpAJ+VasQnhRtavenCUfypUqt9cXNs9r+3AMUo5AqsPO2YeV8Nz6/VGjHwD+sXwn+1s6+cG5cxKS9pyT5mCXv11akDHwShUYs/pgPVUrttejFCydOjTjQo7whz5+EGMLcOuBNrq6fcyfkBv12OnjMqkoSO83YLmywSl+qlFEX7O6x+vj0TVVnDJ73IAnCAhDS5LdxtKphWMm0X4kEVE1hMQSAGqJqkmHaKVq6jjT1gvVAvT5NDvrh2eQciQuXliGy+3lxQ39/TjBHGjt5O+v7+CsecUJ8xnkpjtw+ocDD7pSleageZC7/1Zsq+ewspwhq/JMKcwgKzUpZl+VZVKPpVKllOK02eN5e3sD7V09AlniFEYXfc3qr22upb7dzccWiUFdGHuIqBpCxmel4rAr9jaGr1RVNjhJTrJROkQ7xMYakWIV9rV00OnxjWilCuDISfmU5abxaJTMqt+9sAWvT/Ods2YlbC2WwRwGPkzZYrDtv7ZODx/sbWbZtKEb7WKzKRZMyOWDGGMVPqpqITs1KeZK02lzxuP2+nhjiwkx9Hh9VDV1UJF/aJ7UjEZKclIpzOwxqz/0fhVFWSmcOHNofHuCMJyIqBpCbDZFWW5axErVzjqz8+9QLaPmpidTmJkcslJlXTfSlSqbTXHREWWs2FZHbWvosTXrq1t4eE0Vnz52UkKrHlYUgk0x6OrQYNt/K3c24vXpQedT9WXBhFy21LTG1G79qKqZ+eW5MZtlF1XkkZ+RzEsbTbr6vuYOvD4t7b9RhFKKeWXGrF7b1smrm2u5eGEZSYdY5IxwcCB/tUPMhPz0iJ6qygbniO1sGy1MLcoMGatgXTe1aOR/PhctLMOn4YkQY2u01vz06Y3kpSdzw8nTErqObH+lKj8jedBje3LTTfsvXMp4NFZsqyPNYWdRRfwDkCNxxMRcfDp0VlEwnR4vW2ra4tpin2S3cfKscby6uRaP1xeIU5h0iP8PjjYss/r/3tuL16el9SeMWURUDTHleelUh6lUeX2aPQ2uQ/4Ffeo4E6vQ9819e207eemOQUUHDBVTizI5fEJuyBbgCxsO8O6uRr522gyyU4dmzl84rPbfYE3qYDxV3T4d8GjFy/Lt9Rw1OZ+UpKHdmXm4Pxk9mq9q0/5Wun067tyi0+aMp7Wzm/d2NQbiFKT9N7qwzOq3vbGDhRNzR9wCIAgDRUTVEFOel0Z9uztkK2Nfcwdur48ph7qoKsqkpcNDQ5/MpB11I29SD+biI8rYtL+VTUEjNLq6vfzyuU3MGJ/JlUcm/mw61z8IebAmdYA8a+zNAMzq+5o72FnnHLIohWAKMlOYmJ8e1VdleW7mRxhPE4rjpheSkmTjpY0H2N3gIs1hpyhr5IW70INlVne5vZKgLoxpRFQNMVasQqisqp3Wzr9DfOeRdRbad1zNzlEmqs47vJQkm+o1ZPnfb+9md4OL750zZ1g8H4FK1RBU73IGMapmxXYzmmao/VQWCybkRq1Ufbi3hcLMZEpy4puTlp6cxHHTi3hp4wEq602cggQYji4ss3qqw8Y580tGejmCMGBEVA0xVqxCqHE1lVZG1SjwDI0klmcq2FfV7HJT3+4eVWX//IxkTpw5jifWmrE1De1d/PnVbZwwo4gTZgzPzqSe9t/gK1W5aYMQVdvqKcxMYeb4rEGvIxRHTMylprWTmpbQGwMA1lXHZ1IP5vQ546lu7uDtHQ1iUh+FKKX45JIK/u/k6WQluKUuCIlk6GZqCIAZqgyEjFXYVe8kI9lO0SjwDI0kpTlppDnsvXYAWhELVo7VaOHihWW8vOkAb++o58UNB3C5vXz/nNnD9vzW7r+hEFV5/sdoirP95/Np3tpez3HTCxNW4VngD/Ncu7eJM3P6VyqcXd1sr23nrHkDq2KcPHscSkGHx3vIV4pHK189dcZIL0EQBo1UqoYYq4QdKlZhV72TyUUZh3zrwWZTTCnK6JVVtaPWVK2mFSWmEjJQTp41juzUJP708jbuf28PHz9qItMTVK0JhZUoXRxnyysUgUpVnFlVm2paaXC6WTY9cdW5OaXZJNttYX1VG/a14tNw+ISBDdctzExhkT+9faJUqgRBSBAiqoYYpRTleelhK1VylmzoO1h5e107yUk2yvJGVyhqqsPOOfNLWb27ifRkO187bXjPpotzUnnw88dw/oLSQT+W5alqibNStWKb3081hKGffUlJsjO7NDvsuJqPqsz1h5XlDvg5Tp87HoCKfPkfFAQhMYioSgAT8tKoau5dqXJ3m3l3h/rOP4tp4zKpbu6gw7+9f0dtO1MKMwadxZQILl1khoh++eTp5A9BGy5ehirGICXJTnqynaY4PVUrttfz/+3df2zcdR3H8de77d161467K+s22G6Djc3J2C9Afqgsk41NEwTERE1ATZSYKIlGNAECSlyCEqMmxoQ/iMQQoyYY5UdIkBkjMgyKkGHHkB+DdWsZHW3XXtd27frj4x/3vf5cly39fu/7ae/5SJbdXZP1s7zTu1c/P96fNYvrQ5ktO5Mt+az2txY0PDI67WtNrQVdmKmd1am9L1yZ1ze3rdbHLg63zxYAlBCqInC6maojx/s16mg6WFI65fdeR3G26mB7r1Z7tEl9oitW5rTnu1t1x3UXxz2UWSve/3f2oWpgaEQvHzoe2am/ibasyOrk0IjeOnZi2teaWru14Rz7U02VTSd196fXhd5nCwBKCFURyDekVDg5pJ6B8Q+v0kXKld5NvWTixcoDQyNqOd7vVTuFqdYuWTgv9sJl0kkVTp798t+rh7s0ODwa6dJfyfhm9e5Jrxf6h9Tc2X/O/akAoNwIVRFYHpwAbJ0wW9VMqJrkovPrVGXFtgqHO4uzeD61U5ivcunEOS3/7X2nQzVVpqtXnR/hqIpWNKTVUJfUa1M2q483/ZzdTBUARI1QFYFSW4WJJwDf6+hTLp2Y9aW480Vtolr5hrTe/bB3wkXKBM6ole7/O1svHmzX5Styql8QffcVMzttE9D/BpvUN85ikzoAlAOhKgLjDUAnz1Sxn2qySxrr9W57r95t75WZtGoRM1VRy6SSKpxlS4U323p04GhPJFfTzGRzPquD7b2Tls73txa08vz02OlFAPAVoSoC2XRC9Qtq1HJ8fKbqUEcfS39TrF5cr/c6+vT2sRNalk0plWQDcdRy6eJG9amXWU81Mup0z5/2K5dO6vZrVpZpdMVQ5ZzU1FIYe62ptZv9VADmBEJVBIq9qlJqDWaq+k8Nq61nQBfTo2qS1Y11OjU8qhcPdni9SX0+yaYTGh516h2cfuH3RL/792G91tKtH9546Vgn9nLYNKGzuiS1nxjU0cKANi5jPxUA/xGqIrI8lx7bU9XcUfy70u/8m6q0Mb27f4hN6mWSTRUD0pnaKrQVBvTTv7yl69Ys0s0hNB09F5lUQqsb68b2Ve1/v/g3m9QBzAWEqojkG4ozVc45NXcWT/7RTX2yiXuomKkqj9JdgmcKVQ88/bqGR0f14C0bYmkjsTmf074j3XLOqam1IDNpPTNVAOYAQlVElufS6h0cVnf/ED2qZpCrS45dFMzJv/IonT7tnqFX1XMH2vTcgWP6zva1sd2Rt3lFVp19p9TadVJNrQVd0lhfltOHADBbhKqI5IMTgK1dJ3Woo0+LFy5QHR8M05S6qLP8Vx5nmqk6MTCkB546oHVLF8baPX5LsK9qX0u3mloLs+6kDgDlQqiKSKkBaEtXPyf/zuCyCzO6IFMby516lWg8VE2fqfr5nrd17MSAHvr8RiWq43tr+MjShapNVOnZ/R+oo3dQmzj5B2COIFRFZHlDaaaqX82Eqhl9f9daPfGtT8yLK2Dmgkzq9DNV+4506bGXmvXVay8auy4mLonqKm1YltGeN45JEjNVAOYMQlVEzqtNKJNK6MDRHnX2nSJUzSCdrNHSTG3cw6gYC2qqlU5Wq3tCA9ChkVHd++f9WrKwVt/buTbG0Y3bnM9qZNSppsp06QXnxT0cADgrhKoI5RtS+ufBDkmimzq8kUsn1TVh+e/Xew/pzbYT2n3zei2s9aNr+eZ8TlLxIuvaBE1hAcwNhKoILc+m1dFb/PBaRaiCJzKphArB8t/hzj798m9va9f6Jdq5fmnMIxu3ZUVWkrQpz9IfgLmD42gRygf7qsykfEM8x9OBqbLphLpPFq+quf/J11VTVaUf3XRZ3MOa5IJMre66Ya22f3Rx3EMBgLNGqIpQKUgty6ZYwoA3cumk/tfWo6deO6q973Ro983rvdvXZmb69vY1cQ8DAM4Jy38RWh70qmKTOnySSSf0Yc+gdj/zhrasyOq2q8t3YTIAzGfMVEUoH/SqIlTBJ9lUQr2Dw6oZMv3k1g2qrqKdBQCEgZmqCOUb0mqoS+qKlbm4hwKMyQVX1Xxj6yqtW0q7AgAICzNVEapNVOuV+3aIvpbwyafWLdahzj72LAFAyAhVEatiaQWeuWRxvX78uQ1xDwMA5h2W/wAAAEJAqAIAAAgBoQoAACAEhCoAAIAQEKoAAABCQKgCAAAIAaEKAAAgBIQqAACAEBCqAAAAQkCoAgAACAGhCgAAIASEKgAAgBAQqgAAAEJgzrl4B2DWLulwGb7VIkkdZfg+OHfUxm/Ux1/Uxm/Ux1+zqc1K51zj6b4Qe6gqFzN7xTl3ZdzjwHTUxm/Ux1/Uxm/Ux19R1YblPwAAgBAQqgAAAEJQSaHqkbgHgBlRG79RH39RG79RH39FUpuK2VMFAAAQpUqaqQIAAIgMoQoA5hAzazCzG8xsUdxjATBZRYQqM3vUzF4ys/vjHguKzGyJme2d8JwaecDMMmb2rJntMbMnzCxJbfxhZjlJz0i6StLfzayR+vgleG/bFzymNh4wsxozO2Jmzwd/NkRVm3kfqszsVknVzrlrJa0yszVxj6nSBR8Mj0mqC55TI3/cJukXzrmdktokfUnUxicbJd3lnHtQ0nOSrhf18c3PJKV4X/PKRkl/cM5tc85tk7RGEdVm3ocqSdskPR483iPpk/ENBYERSV+U1BM83yZq5AXn3MPOub8GTxsl3S5q4w3n3D+cc/8ys60qzlbtEvXxhpldL6lPxV9Itona+OIaSTea2ctm9qikHYqoNpUQquokvR88Pi5pSYxjgSTnXI9zrjDhJWrkGTO7VlJOUouojVfMzFT8paRLkhP18YKZJSX9QNI9wUu8r/njP5J2OOeukpSQ9BlFVJtKCFW9klLB43pVxv95rqFGHjGzBkm/kvQ1URvvuKI7JTVJ+riojy/ukfSwc647eM7Pjj+anHMfBI9fUfHev0hqUwlFflXjU3ubJDXHNxTMgBp5Ivht+4+S7nXOHRa18YqZ3W1mXwmeZiU9JOrjix2S7jSz5yVtlvRZURtf/NbMNplZtaRbJN2piGpTE9Y/5LEnJe01swtVnPK7Jt7h4DSeFDXyxdclXS7pPjO7T9JvJH2Z2njjEUmPm9kdkl5X8WfnBeoTP+fc1tLjIFjdJN7XfLFb0u8lmaSnFeFnTkV0VA9Om90g6QXnXFvc48F01Mhf1MZv1Mdf1MZfUdWmIkIVAABA1CphTxUAAEDkCFUAAAAhIFQBAACEgFAFAAAQAkIVAABACP4PXwuMmNCZT/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "train_true_pred = pd.read_csv('../tmp/train_true_pred.csv', encoding='gbk')\n",
    "train_true_pred = train_true_pred.iloc[:50, :]\n",
    "x = range(len(train_true_pred))\n",
    "y_true = train_true_pred['true']  # 提取血糖真实值\n",
    "y_pred = train_true_pred['pred']  # 提取血糖预测值\n",
    "\n",
    "# 绘制部分训练数据的血糖的真实值与预测值的折线图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y_true)\n",
    "plt.plot(x, y_pred)\n",
    "# 设置rc参数显示中文标题\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置字体为SimHei显示中文\n",
    "plt.ylabel('血糖')\n",
    "plt.title('部分训练数据真实值与预测值结果的折线图')\n",
    "plt.legend((\"真实值\",\"预测值\"), loc=1)\n",
    "plt.savefig('../tmp/部分训练数据真实值与预测值结果的折线图.jpg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>性别</th>\n",
       "      <th>年龄</th>\n",
       "      <th>体检日期</th>\n",
       "      <th>*天门冬氨酸氨基转换酶</th>\n",
       "      <th>*丙氨酸氨基转换酶</th>\n",
       "      <th>*碱性磷酸酶</th>\n",
       "      <th>*r-谷氨酰基转换酶</th>\n",
       "      <th>*总蛋白</th>\n",
       "      <th>白蛋白</th>\n",
       "      <th>...</th>\n",
       "      <th>血小板计数</th>\n",
       "      <th>血小板平均体积</th>\n",
       "      <th>血小板体积分布宽度</th>\n",
       "      <th>血小板比积</th>\n",
       "      <th>中性粒细胞%</th>\n",
       "      <th>淋巴细胞%</th>\n",
       "      <th>单核细胞%</th>\n",
       "      <th>嗜酸细胞%</th>\n",
       "      <th>嗜碱细胞%</th>\n",
       "      <th>血糖</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5768.0</td>\n",
       "      <td>男</td>\n",
       "      <td>54.0</td>\n",
       "      <td>13/10/2017</td>\n",
       "      <td>38.27</td>\n",
       "      <td>57.04</td>\n",
       "      <td>110.07</td>\n",
       "      <td>76.15</td>\n",
       "      <td>79.15</td>\n",
       "      <td>45.29</td>\n",
       "      <td>...</td>\n",
       "      <td>233.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.280</td>\n",
       "      <td>52.1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>7.339150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5781.0</td>\n",
       "      <td>男</td>\n",
       "      <td>29.0</td>\n",
       "      <td>13/10/2017</td>\n",
       "      <td>19.44</td>\n",
       "      <td>24.42</td>\n",
       "      <td>84.67</td>\n",
       "      <td>40.63</td>\n",
       "      <td>78.22</td>\n",
       "      <td>47.39</td>\n",
       "      <td>...</td>\n",
       "      <td>264.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.260</td>\n",
       "      <td>66.9</td>\n",
       "      <td>24.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6.888713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5805.0</td>\n",
       "      <td>男</td>\n",
       "      <td>53.0</td>\n",
       "      <td>13/10/2017</td>\n",
       "      <td>18.54</td>\n",
       "      <td>17.73</td>\n",
       "      <td>92.63</td>\n",
       "      <td>21.31</td>\n",
       "      <td>73.60</td>\n",
       "      <td>48.85</td>\n",
       "      <td>...</td>\n",
       "      <td>179.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.180</td>\n",
       "      <td>61.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>6.957505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5877.0</td>\n",
       "      <td>女</td>\n",
       "      <td>49.0</td>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>23.28</td>\n",
       "      <td>30.40</td>\n",
       "      <td>55.17</td>\n",
       "      <td>45.19</td>\n",
       "      <td>73.96</td>\n",
       "      <td>42.22</td>\n",
       "      <td>...</td>\n",
       "      <td>246.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.270</td>\n",
       "      <td>58.2</td>\n",
       "      <td>25.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.736703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>5910.0</td>\n",
       "      <td>女</td>\n",
       "      <td>62.0</td>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>48.89</td>\n",
       "      <td>94.56</td>\n",
       "      <td>101.23</td>\n",
       "      <td>246.25</td>\n",
       "      <td>88.88</td>\n",
       "      <td>53.02</td>\n",
       "      <td>...</td>\n",
       "      <td>228.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.270</td>\n",
       "      <td>39.6</td>\n",
       "      <td>49.5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>6.874014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>5957.0</td>\n",
       "      <td>男</td>\n",
       "      <td>47.0</td>\n",
       "      <td>26/10/2017</td>\n",
       "      <td>44.53</td>\n",
       "      <td>63.28</td>\n",
       "      <td>88.20</td>\n",
       "      <td>45.79</td>\n",
       "      <td>93.11</td>\n",
       "      <td>51.16</td>\n",
       "      <td>...</td>\n",
       "      <td>212.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.240</td>\n",
       "      <td>53.3</td>\n",
       "      <td>39.1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.788585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>5985.0</td>\n",
       "      <td>女</td>\n",
       "      <td>58.0</td>\n",
       "      <td>12/10/2017</td>\n",
       "      <td>23.28</td>\n",
       "      <td>17.96</td>\n",
       "      <td>106.32</td>\n",
       "      <td>69.96</td>\n",
       "      <td>84.44</td>\n",
       "      <td>47.31</td>\n",
       "      <td>...</td>\n",
       "      <td>201.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>0.240</td>\n",
       "      <td>72.9</td>\n",
       "      <td>20.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6.940640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>6005.0</td>\n",
       "      <td>女</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12/10/2017</td>\n",
       "      <td>17.36</td>\n",
       "      <td>15.42</td>\n",
       "      <td>90.97</td>\n",
       "      <td>30.11</td>\n",
       "      <td>79.88</td>\n",
       "      <td>42.22</td>\n",
       "      <td>...</td>\n",
       "      <td>277.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.300</td>\n",
       "      <td>63.9</td>\n",
       "      <td>28.7</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.341627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>6032.0</td>\n",
       "      <td>男</td>\n",
       "      <td>56.0</td>\n",
       "      <td>12/10/2017</td>\n",
       "      <td>34.89</td>\n",
       "      <td>52.48</td>\n",
       "      <td>115.98</td>\n",
       "      <td>61.43</td>\n",
       "      <td>79.71</td>\n",
       "      <td>45.10</td>\n",
       "      <td>...</td>\n",
       "      <td>278.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.290</td>\n",
       "      <td>53.5</td>\n",
       "      <td>37.4</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.209972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>6033.0</td>\n",
       "      <td>男</td>\n",
       "      <td>53.0</td>\n",
       "      <td>12/10/2017</td>\n",
       "      <td>21.47</td>\n",
       "      <td>24.67</td>\n",
       "      <td>94.12</td>\n",
       "      <td>63.03</td>\n",
       "      <td>74.41</td>\n",
       "      <td>43.53</td>\n",
       "      <td>...</td>\n",
       "      <td>156.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.170</td>\n",
       "      <td>62.4</td>\n",
       "      <td>27.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.151437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>6044.0</td>\n",
       "      <td>男</td>\n",
       "      <td>47.0</td>\n",
       "      <td>12/10/2017</td>\n",
       "      <td>28.93</td>\n",
       "      <td>67.86</td>\n",
       "      <td>114.29</td>\n",
       "      <td>43.58</td>\n",
       "      <td>75.85</td>\n",
       "      <td>46.60</td>\n",
       "      <td>...</td>\n",
       "      <td>158.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.180</td>\n",
       "      <td>51.3</td>\n",
       "      <td>39.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>7.224646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>6054.0</td>\n",
       "      <td>男</td>\n",
       "      <td>33.0</td>\n",
       "      <td>12/10/2017</td>\n",
       "      <td>30.03</td>\n",
       "      <td>67.44</td>\n",
       "      <td>90.56</td>\n",
       "      <td>626.89</td>\n",
       "      <td>82.57</td>\n",
       "      <td>48.59</td>\n",
       "      <td>...</td>\n",
       "      <td>196.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>17.9</td>\n",
       "      <td>0.240</td>\n",
       "      <td>48.7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.407838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>6072.0</td>\n",
       "      <td>男</td>\n",
       "      <td>53.0</td>\n",
       "      <td>18/10/2017</td>\n",
       "      <td>65.01</td>\n",
       "      <td>89.08</td>\n",
       "      <td>158.71</td>\n",
       "      <td>60.18</td>\n",
       "      <td>78.83</td>\n",
       "      <td>47.56</td>\n",
       "      <td>...</td>\n",
       "      <td>192.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>0.154</td>\n",
       "      <td>63.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>6.710933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>6145.0</td>\n",
       "      <td>女</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13/10/2017</td>\n",
       "      <td>62.71</td>\n",
       "      <td>49.73</td>\n",
       "      <td>132.51</td>\n",
       "      <td>34.91</td>\n",
       "      <td>84.67</td>\n",
       "      <td>45.14</td>\n",
       "      <td>...</td>\n",
       "      <td>165.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.150</td>\n",
       "      <td>52.0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.750280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>6150.0</td>\n",
       "      <td>男</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13/10/2017</td>\n",
       "      <td>35.27</td>\n",
       "      <td>29.12</td>\n",
       "      <td>140.41</td>\n",
       "      <td>70.27</td>\n",
       "      <td>72.67</td>\n",
       "      <td>43.40</td>\n",
       "      <td>...</td>\n",
       "      <td>160.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.158</td>\n",
       "      <td>58.7</td>\n",
       "      <td>29.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>6.958941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>6155.0</td>\n",
       "      <td>男</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13/10/2017</td>\n",
       "      <td>28.49</td>\n",
       "      <td>39.89</td>\n",
       "      <td>109.92</td>\n",
       "      <td>65.45</td>\n",
       "      <td>80.96</td>\n",
       "      <td>50.30</td>\n",
       "      <td>...</td>\n",
       "      <td>240.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>0.240</td>\n",
       "      <td>65.5</td>\n",
       "      <td>25.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6.876706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>6183.0</td>\n",
       "      <td>男</td>\n",
       "      <td>58.0</td>\n",
       "      <td>17/10/2017</td>\n",
       "      <td>20.85</td>\n",
       "      <td>28.54</td>\n",
       "      <td>50.78</td>\n",
       "      <td>31.66</td>\n",
       "      <td>68.32</td>\n",
       "      <td>44.96</td>\n",
       "      <td>...</td>\n",
       "      <td>187.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.210</td>\n",
       "      <td>56.6</td>\n",
       "      <td>29.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.808386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>6206.0</td>\n",
       "      <td>男</td>\n",
       "      <td>54.0</td>\n",
       "      <td>26/10/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>221.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.220</td>\n",
       "      <td>71.3</td>\n",
       "      <td>19.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>6.792893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>6207.0</td>\n",
       "      <td>男</td>\n",
       "      <td>54.0</td>\n",
       "      <td>26/10/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>249.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.260</td>\n",
       "      <td>66.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.363349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>6313.0</td>\n",
       "      <td>男</td>\n",
       "      <td>55.0</td>\n",
       "      <td>16/10/2017</td>\n",
       "      <td>23.99</td>\n",
       "      <td>15.28</td>\n",
       "      <td>114.27</td>\n",
       "      <td>36.51</td>\n",
       "      <td>76.13</td>\n",
       "      <td>46.13</td>\n",
       "      <td>...</td>\n",
       "      <td>187.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.190</td>\n",
       "      <td>58.8</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.675898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>6318.0</td>\n",
       "      <td>男</td>\n",
       "      <td>65.0</td>\n",
       "      <td>16/10/2017</td>\n",
       "      <td>25.33</td>\n",
       "      <td>30.06</td>\n",
       "      <td>81.95</td>\n",
       "      <td>16.74</td>\n",
       "      <td>77.04</td>\n",
       "      <td>47.91</td>\n",
       "      <td>...</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.240</td>\n",
       "      <td>60.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.892145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>6331.0</td>\n",
       "      <td>男</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16/10/2017</td>\n",
       "      <td>18.46</td>\n",
       "      <td>22.04</td>\n",
       "      <td>116.48</td>\n",
       "      <td>28.20</td>\n",
       "      <td>79.11</td>\n",
       "      <td>49.92</td>\n",
       "      <td>...</td>\n",
       "      <td>231.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.240</td>\n",
       "      <td>65.7</td>\n",
       "      <td>26.4</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.743322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>6339.0</td>\n",
       "      <td>男</td>\n",
       "      <td>54.0</td>\n",
       "      <td>16/10/2017</td>\n",
       "      <td>25.70</td>\n",
       "      <td>40.41</td>\n",
       "      <td>90.86</td>\n",
       "      <td>53.78</td>\n",
       "      <td>75.03</td>\n",
       "      <td>47.34</td>\n",
       "      <td>...</td>\n",
       "      <td>299.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.310</td>\n",
       "      <td>64.3</td>\n",
       "      <td>27.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6.993505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>6353.0</td>\n",
       "      <td>男</td>\n",
       "      <td>59.0</td>\n",
       "      <td>16/10/2017</td>\n",
       "      <td>36.18</td>\n",
       "      <td>39.99</td>\n",
       "      <td>122.06</td>\n",
       "      <td>29.84</td>\n",
       "      <td>80.52</td>\n",
       "      <td>49.69</td>\n",
       "      <td>...</td>\n",
       "      <td>179.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.190</td>\n",
       "      <td>54.0</td>\n",
       "      <td>36.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.893274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>6354.0</td>\n",
       "      <td>女</td>\n",
       "      <td>41.0</td>\n",
       "      <td>16/10/2017</td>\n",
       "      <td>17.91</td>\n",
       "      <td>15.90</td>\n",
       "      <td>94.97</td>\n",
       "      <td>27.22</td>\n",
       "      <td>79.79</td>\n",
       "      <td>44.06</td>\n",
       "      <td>...</td>\n",
       "      <td>250.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>0.250</td>\n",
       "      <td>67.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.753247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>6382.0</td>\n",
       "      <td>男</td>\n",
       "      <td>65.0</td>\n",
       "      <td>17/10/2017</td>\n",
       "      <td>48.56</td>\n",
       "      <td>67.64</td>\n",
       "      <td>117.40</td>\n",
       "      <td>71.56</td>\n",
       "      <td>77.57</td>\n",
       "      <td>46.02</td>\n",
       "      <td>...</td>\n",
       "      <td>218.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.220</td>\n",
       "      <td>44.9</td>\n",
       "      <td>42.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.391726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>6441.0</td>\n",
       "      <td>女</td>\n",
       "      <td>61.0</td>\n",
       "      <td>17/10/2017</td>\n",
       "      <td>20.61</td>\n",
       "      <td>21.05</td>\n",
       "      <td>104.86</td>\n",
       "      <td>25.49</td>\n",
       "      <td>72.65</td>\n",
       "      <td>44.02</td>\n",
       "      <td>...</td>\n",
       "      <td>187.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.200</td>\n",
       "      <td>51.9</td>\n",
       "      <td>41.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>8.437156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>6480.0</td>\n",
       "      <td>男</td>\n",
       "      <td>64.0</td>\n",
       "      <td>17/10/2017</td>\n",
       "      <td>42.28</td>\n",
       "      <td>47.55</td>\n",
       "      <td>105.34</td>\n",
       "      <td>37.50</td>\n",
       "      <td>76.18</td>\n",
       "      <td>44.57</td>\n",
       "      <td>...</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.240</td>\n",
       "      <td>47.2</td>\n",
       "      <td>42.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>6.958663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>6582.0</td>\n",
       "      <td>男</td>\n",
       "      <td>46.0</td>\n",
       "      <td>18/10/2017</td>\n",
       "      <td>28.46</td>\n",
       "      <td>21.66</td>\n",
       "      <td>82.68</td>\n",
       "      <td>91.44</td>\n",
       "      <td>72.83</td>\n",
       "      <td>44.85</td>\n",
       "      <td>...</td>\n",
       "      <td>216.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.174</td>\n",
       "      <td>61.5</td>\n",
       "      <td>26.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.924622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>6635.0</td>\n",
       "      <td>男</td>\n",
       "      <td>58.0</td>\n",
       "      <td>19/10/2017</td>\n",
       "      <td>15.29</td>\n",
       "      <td>17.72</td>\n",
       "      <td>118.05</td>\n",
       "      <td>17.78</td>\n",
       "      <td>71.36</td>\n",
       "      <td>46.80</td>\n",
       "      <td>...</td>\n",
       "      <td>237.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.270</td>\n",
       "      <td>62.0</td>\n",
       "      <td>28.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>7.194679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>6679.0</td>\n",
       "      <td>男</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19/10/2017</td>\n",
       "      <td>41.69</td>\n",
       "      <td>45.73</td>\n",
       "      <td>121.34</td>\n",
       "      <td>137.89</td>\n",
       "      <td>84.37</td>\n",
       "      <td>42.98</td>\n",
       "      <td>...</td>\n",
       "      <td>176.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.170</td>\n",
       "      <td>53.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>11.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.732719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>6694.0</td>\n",
       "      <td>男</td>\n",
       "      <td>52.0</td>\n",
       "      <td>19/10/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.210</td>\n",
       "      <td>69.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>7.525680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>6696.0</td>\n",
       "      <td>男</td>\n",
       "      <td>54.0</td>\n",
       "      <td>19/10/2017</td>\n",
       "      <td>38.20</td>\n",
       "      <td>29.90</td>\n",
       "      <td>63.59</td>\n",
       "      <td>158.29</td>\n",
       "      <td>81.40</td>\n",
       "      <td>48.21</td>\n",
       "      <td>...</td>\n",
       "      <td>235.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.270</td>\n",
       "      <td>66.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>6.892954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>6705.0</td>\n",
       "      <td>女</td>\n",
       "      <td>78.0</td>\n",
       "      <td>19/10/2017</td>\n",
       "      <td>20.68</td>\n",
       "      <td>20.52</td>\n",
       "      <td>92.92</td>\n",
       "      <td>48.06</td>\n",
       "      <td>84.09</td>\n",
       "      <td>48.37</td>\n",
       "      <td>...</td>\n",
       "      <td>315.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>0.360</td>\n",
       "      <td>48.5</td>\n",
       "      <td>46.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.376654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>6707.0</td>\n",
       "      <td>男</td>\n",
       "      <td>54.0</td>\n",
       "      <td>19/10/2017</td>\n",
       "      <td>27.33</td>\n",
       "      <td>29.50</td>\n",
       "      <td>66.61</td>\n",
       "      <td>66.63</td>\n",
       "      <td>75.90</td>\n",
       "      <td>45.25</td>\n",
       "      <td>...</td>\n",
       "      <td>203.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.230</td>\n",
       "      <td>43.5</td>\n",
       "      <td>44.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>7.179272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>6720.0</td>\n",
       "      <td>男</td>\n",
       "      <td>55.0</td>\n",
       "      <td>20/10/2017</td>\n",
       "      <td>36.74</td>\n",
       "      <td>21.48</td>\n",
       "      <td>130.25</td>\n",
       "      <td>232.99</td>\n",
       "      <td>77.96</td>\n",
       "      <td>43.41</td>\n",
       "      <td>...</td>\n",
       "      <td>210.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.220</td>\n",
       "      <td>54.2</td>\n",
       "      <td>37.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.721622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>6728.0</td>\n",
       "      <td>男</td>\n",
       "      <td>62.0</td>\n",
       "      <td>19/10/2017</td>\n",
       "      <td>66.12</td>\n",
       "      <td>83.58</td>\n",
       "      <td>65.21</td>\n",
       "      <td>166.35</td>\n",
       "      <td>76.76</td>\n",
       "      <td>45.15</td>\n",
       "      <td>...</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>0.240</td>\n",
       "      <td>57.1</td>\n",
       "      <td>34.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.155155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>6737.0</td>\n",
       "      <td>男</td>\n",
       "      <td>57.0</td>\n",
       "      <td>20/10/2017</td>\n",
       "      <td>21.65</td>\n",
       "      <td>20.43</td>\n",
       "      <td>131.92</td>\n",
       "      <td>31.19</td>\n",
       "      <td>72.86</td>\n",
       "      <td>47.83</td>\n",
       "      <td>...</td>\n",
       "      <td>259.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.290</td>\n",
       "      <td>63.9</td>\n",
       "      <td>26.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.028971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>6740.0</td>\n",
       "      <td>男</td>\n",
       "      <td>56.0</td>\n",
       "      <td>20/10/2017</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.20</td>\n",
       "      <td>73.02</td>\n",
       "      <td>29.87</td>\n",
       "      <td>80.89</td>\n",
       "      <td>50.00</td>\n",
       "      <td>...</td>\n",
       "      <td>228.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.250</td>\n",
       "      <td>76.3</td>\n",
       "      <td>16.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6.958305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>6742.0</td>\n",
       "      <td>男</td>\n",
       "      <td>54.0</td>\n",
       "      <td>20/10/2017</td>\n",
       "      <td>26.21</td>\n",
       "      <td>44.90</td>\n",
       "      <td>82.81</td>\n",
       "      <td>50.35</td>\n",
       "      <td>76.37</td>\n",
       "      <td>47.32</td>\n",
       "      <td>...</td>\n",
       "      <td>149.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.210</td>\n",
       "      <td>54.7</td>\n",
       "      <td>32.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.456942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>6763.0</td>\n",
       "      <td>男</td>\n",
       "      <td>54.0</td>\n",
       "      <td>20/10/2017</td>\n",
       "      <td>30.47</td>\n",
       "      <td>38.47</td>\n",
       "      <td>132.95</td>\n",
       "      <td>28.20</td>\n",
       "      <td>78.42</td>\n",
       "      <td>46.59</td>\n",
       "      <td>...</td>\n",
       "      <td>293.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.320</td>\n",
       "      <td>45.5</td>\n",
       "      <td>44.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.879148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>6766.0</td>\n",
       "      <td>男</td>\n",
       "      <td>79.0</td>\n",
       "      <td>20/10/2017</td>\n",
       "      <td>18.50</td>\n",
       "      <td>13.24</td>\n",
       "      <td>72.54</td>\n",
       "      <td>27.82</td>\n",
       "      <td>80.06</td>\n",
       "      <td>48.16</td>\n",
       "      <td>...</td>\n",
       "      <td>258.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.250</td>\n",
       "      <td>55.7</td>\n",
       "      <td>31.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.041890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id 性别    年龄        体检日期  *天门冬氨酸氨基转换酶  *丙氨酸氨基转换酶  *碱性磷酸酶  *r-谷氨酰基转换酶  \\\n",
       "33   5768.0  男  54.0  13/10/2017        38.27      57.04  110.07       76.15   \n",
       "46   5781.0  男  29.0  13/10/2017        19.44      24.42   84.67       40.63   \n",
       "70   5805.0  男  53.0  13/10/2017        18.54      17.73   92.63       21.31   \n",
       "142  5877.0  女  49.0  11/10/2017        23.28      30.40   55.17       45.19   \n",
       "173  5910.0  女  62.0  11/10/2017        48.89      94.56  101.23      246.25   \n",
       "220  5957.0  男  47.0  26/10/2017        44.53      63.28   88.20       45.79   \n",
       "247  5985.0  女  58.0  12/10/2017        23.28      17.96  106.32       69.96   \n",
       "267  6005.0  女  61.0  12/10/2017        17.36      15.42   90.97       30.11   \n",
       "292  6032.0  男  56.0  12/10/2017        34.89      52.48  115.98       61.43   \n",
       "293  6033.0  男  53.0  12/10/2017        21.47      24.67   94.12       63.03   \n",
       "303  6044.0  男  47.0  12/10/2017        28.93      67.86  114.29       43.58   \n",
       "313  6054.0  男  33.0  12/10/2017        30.03      67.44   90.56      626.89   \n",
       "330  6072.0  男  53.0  18/10/2017        65.01      89.08  158.71       60.18   \n",
       "398  6145.0  女  74.0  13/10/2017        62.71      49.73  132.51       34.91   \n",
       "403  6150.0  男  60.0  13/10/2017        35.27      29.12  140.41       70.27   \n",
       "408  6155.0  男  50.0  13/10/2017        28.49      39.89  109.92       65.45   \n",
       "436  6183.0  男  58.0  17/10/2017        20.85      28.54   50.78       31.66   \n",
       "459  6206.0  男  54.0  26/10/2017          NaN        NaN     NaN         NaN   \n",
       "460  6207.0  男  54.0  26/10/2017          NaN        NaN     NaN         NaN   \n",
       "564  6313.0  男  55.0  16/10/2017        23.99      15.28  114.27       36.51   \n",
       "569  6318.0  男  65.0  16/10/2017        25.33      30.06   81.95       16.74   \n",
       "582  6331.0  男  45.0  16/10/2017        18.46      22.04  116.48       28.20   \n",
       "589  6339.0  男  54.0  16/10/2017        25.70      40.41   90.86       53.78   \n",
       "601  6353.0  男  59.0  16/10/2017        36.18      39.99  122.06       29.84   \n",
       "602  6354.0  女  41.0  16/10/2017        17.91      15.90   94.97       27.22   \n",
       "628  6382.0  男  65.0  17/10/2017        48.56      67.64  117.40       71.56   \n",
       "684  6441.0  女  61.0  17/10/2017        20.61      21.05  104.86       25.49   \n",
       "722  6480.0  男  64.0  17/10/2017        42.28      47.55  105.34       37.50   \n",
       "822  6582.0  男  46.0  18/10/2017        28.46      21.66   82.68       91.44   \n",
       "871  6635.0  男  58.0  19/10/2017        15.29      17.72  118.05       17.78   \n",
       "914  6679.0  男  80.0  19/10/2017        41.69      45.73  121.34      137.89   \n",
       "928  6694.0  男  52.0  19/10/2017          NaN        NaN     NaN         NaN   \n",
       "930  6696.0  男  54.0  19/10/2017        38.20      29.90   63.59      158.29   \n",
       "938  6705.0  女  78.0  19/10/2017        20.68      20.52   92.92       48.06   \n",
       "940  6707.0  男  54.0  19/10/2017        27.33      29.50   66.61       66.63   \n",
       "951  6720.0  男  55.0  20/10/2017        36.74      21.48  130.25      232.99   \n",
       "959  6728.0  男  62.0  19/10/2017        66.12      83.58   65.21      166.35   \n",
       "968  6737.0  男  57.0  20/10/2017        21.65      20.43  131.92       31.19   \n",
       "971  6740.0  男  56.0  20/10/2017        18.05      18.20   73.02       29.87   \n",
       "973  6742.0  男  54.0  20/10/2017        26.21      44.90   82.81       50.35   \n",
       "994  6763.0  男  54.0  20/10/2017        30.47      38.47  132.95       28.20   \n",
       "997  6766.0  男  79.0  20/10/2017        18.50      13.24   72.54       27.82   \n",
       "\n",
       "      *总蛋白    白蛋白  ...  血小板计数  血小板平均体积  血小板体积分布宽度  血小板比积  中性粒细胞%  淋巴细胞%  \\\n",
       "33   79.15  45.29  ...  233.0     12.0       15.6  0.280    52.1   36.0   \n",
       "46   78.22  47.39  ...  264.0      9.8       11.9  0.260    66.9   24.6   \n",
       "70   73.60  48.85  ...  179.0     10.0       11.9  0.180    61.9   26.0   \n",
       "142  73.96  42.22  ...  246.0     10.9       13.2  0.270    58.2   25.5   \n",
       "173  88.88  53.02  ...  228.0     11.7       13.7  0.270    39.6   49.5   \n",
       "220  93.11  51.16  ...  212.0     11.4       13.8  0.240    53.3   39.1   \n",
       "247  84.44  47.31  ...  201.0     12.0       17.6  0.240    72.9   20.2   \n",
       "267  79.88  42.22  ...  277.0     10.7       13.5  0.300    63.9   28.7   \n",
       "292  79.71  45.10  ...  278.0     10.4       11.8  0.290    53.5   37.4   \n",
       "293  74.41  43.53  ...  156.0     11.0       13.9  0.170    62.4   27.3   \n",
       "303  75.85  46.60  ...  158.0     11.4       13.5  0.180    51.3   39.3   \n",
       "313  82.57  48.59  ...  196.0     12.4       17.9  0.240    48.7   39.0   \n",
       "330  78.83  47.56  ...  192.0      8.0       17.1  0.154    63.3   29.7   \n",
       "398  84.67  45.14  ...  165.0      9.1       16.8  0.150    52.0   37.2   \n",
       "403  72.67  43.40  ...  160.0      9.8       18.3  0.158    58.7   29.4   \n",
       "408  80.96  50.30  ...  240.0     10.1       12.1  0.240    65.5   25.2   \n",
       "436  68.32  44.96  ...  187.0     10.9       14.1  0.210    56.6   29.9   \n",
       "459    NaN    NaN  ...  221.0     10.1       11.9  0.220    71.3   19.7   \n",
       "460    NaN    NaN  ...  249.0     10.5       13.5  0.260    66.8   26.5   \n",
       "564  76.13  46.13  ...  187.0     10.3       11.8  0.190    58.8   35.0   \n",
       "569  77.04  47.91  ...  230.0     10.6       12.3  0.240    60.6   26.0   \n",
       "582  79.11  49.92  ...  231.0     10.3       12.0  0.240    65.7   26.4   \n",
       "589  75.03  47.34  ...  299.0     10.5       12.3  0.310    64.3   27.1   \n",
       "601  80.52  49.69  ...  179.0     10.7       13.7  0.190    54.0   36.1   \n",
       "602  79.79  44.06  ...  250.0     10.0       12.1  0.250    67.9   24.8   \n",
       "628  77.57  46.02  ...  218.0     10.0       11.7  0.220    44.9   42.1   \n",
       "684  72.65  44.02  ...  187.0     10.9       13.8  0.200    51.9   41.7   \n",
       "722  76.18  44.57  ...  222.0     11.0       14.0  0.240    47.2   42.3   \n",
       "822  72.83  44.85  ...  216.0      8.1       17.0  0.174    61.5   26.2   \n",
       "871  71.36  46.80  ...  237.0     11.5       13.7  0.270    62.0   28.7   \n",
       "914  84.37  42.98  ...  176.0      9.8       10.6  0.170    53.1   29.2   \n",
       "928    NaN    NaN  ...  169.0     12.4       17.4  0.210    69.5   24.5   \n",
       "930  81.40  48.21  ...  235.0     11.4       14.1  0.270    66.9   22.9   \n",
       "938  84.09  48.37  ...  315.0     11.6       15.4  0.360    48.5   46.1   \n",
       "940  75.90  45.25  ...  203.0     11.3       14.6  0.230    43.5   44.8   \n",
       "951  77.96  43.41  ...  210.0     10.3       12.0  0.220    54.2   37.6   \n",
       "959  76.76  45.15  ...  236.0     10.0       12.1  0.240    57.1   34.9   \n",
       "968  72.86  47.83  ...  259.0     11.1       14.1  0.290    63.9   26.9   \n",
       "971  80.89  50.00  ...  228.0     11.1       13.9  0.250    76.3   16.1   \n",
       "973  76.37  47.32  ...  149.0     13.9       23.5  0.210    54.7   32.2   \n",
       "994  78.42  46.59  ...  293.0     10.9       13.6  0.320    45.5   44.9   \n",
       "997  80.06  48.16  ...  258.0      9.9       11.1  0.250    55.7   31.1   \n",
       "\n",
       "     单核细胞%  嗜酸细胞%  嗜碱细胞%         血糖  \n",
       "33     7.5    3.5    0.9   7.339150  \n",
       "46     5.8    2.3    0.4   6.888713  \n",
       "70     8.6    2.9    0.6   6.957505  \n",
       "142    6.3    8.7    1.3   6.736703  \n",
       "173    6.1    4.0    0.8   6.874014  \n",
       "220    6.6    0.7    0.3   6.788585  \n",
       "247    5.6    1.1    0.2   6.940640  \n",
       "267    5.9    0.9    0.6   7.341627  \n",
       "292    6.9    1.5    0.7   7.209972  \n",
       "293    7.1    2.8    0.4   7.151437  \n",
       "303    6.1    2.4    0.9   7.224646  \n",
       "313    6.2    5.1    1.0   8.407838  \n",
       "330    5.4    0.9    0.8   6.710933  \n",
       "398    8.3    1.8    0.7   6.750280  \n",
       "403    7.7    3.1    1.1   6.958941  \n",
       "408    6.3    2.6    0.4   6.876706  \n",
       "436    9.1    3.7    0.7   6.808386  \n",
       "459    6.5    1.6    0.9   6.792893  \n",
       "460    5.5    0.7    0.5   7.363349  \n",
       "564    5.0    0.8    0.4   7.675898  \n",
       "569    8.1    4.6    0.7   6.892145  \n",
       "582    5.7    1.7    0.5   6.743322  \n",
       "589    7.2    1.2    0.2   6.993505  \n",
       "601    8.0    1.3    0.6   7.893274  \n",
       "602    5.8    1.2    0.3   6.753247  \n",
       "628    9.0    2.8    1.2   7.391726  \n",
       "684    5.4    0.6    0.4   8.437156  \n",
       "722    6.4    3.2    0.9   6.958663  \n",
       "822    8.0    2.7    1.6   6.924622  \n",
       "871    7.2    1.3    0.8   7.194679  \n",
       "914   11.6    5.1    1.0   7.732719  \n",
       "928    5.0    0.8    0.2   7.525680  \n",
       "930    7.3    2.0    0.9   6.892954  \n",
       "938    4.9    0.2    0.3  10.376654  \n",
       "940    5.7    4.9    1.1   7.179272  \n",
       "951    6.8    0.7    0.7   6.721622  \n",
       "959    7.1    0.6    0.3   7.155155  \n",
       "968    6.3    2.5    0.4   7.028971  \n",
       "971    5.1    2.1    0.4   6.958305  \n",
       "973    7.2    5.3    0.6   7.456942  \n",
       "994    7.6    1.3    0.7   6.879148  \n",
       "997   11.2    1.5    0.5   7.041890  \n",
       "\n",
       "[42 rows x 42 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 对预测数据筛选出血糖值在3.9～6.1毫摩尔/升的数据\n",
    "test = pd.read_csv('../data/test.csv', encoding='gbk')\n",
    "test_preds = pd.read_csv('../tmp/test_preds.csv', encoding='gbk')\n",
    "data = pd.concat([test, test_preds], axis=1)\n",
    "df1 = data[data['血糖'] > 6.7]\n",
    "df1 = df1[np.isnan(df1['id']) == False] \n",
    "df1.to_csv('../tmp/hyperglycemia.csv', index=False, encoding='gbk')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差：  0.25645390964426235\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# 计算均方误差回归损失\n",
    "err1 = mean_squared_error(train_true_pred['true'], train_true_pred['pred'])*0.5\n",
    "print('均方误差：  {}'.format(err1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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